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Design Impact » Transportation

# Part IV: Semi-Autonomous Control Framework: Present Performance and Future Work

Please welcome back Sterling Anderson, a Ph.D. candidate at MIT, for the final post in his series on semi-autonomous driver assistance systems.

We’ve made it! Congratulations to all those who hung on through the first three posts in this series. Having done so, you are better prepared to understand and appreciate what I’m about to show you. For those tuning in for the first time (or you who decided to skip straight to the good stuff), welcome! The demonstration that follows should be sufficiently accessible that you’ll be able to appreciate, at least in part, what we’ve done here. If at any point you find yourself asking the question “wait a minute, don’t some cars already do this?” I would suggest you go back and read Parts 2 and 3 to understand the fundamental advances this framework provides when compared to the existing state of the art.

### SUMMARY OF POSTS 1-3

Vehicular accidents are costly. Not only do they end lives, injure travelers, and destroy assets, but they also inspire excessively large, heavy, and inefficient vehicles. Active safety systems can assist error-prone human drivers in avoiding accidents and thereby improve safety, efficiency, and cost. Active safety systems existing today are fundamentally limited in their inability to accurately quantify threat and intervene in more than one dimension to assist the human driver in avoiding it. As such, these systems must be implemented in an ad-hoc fashion, requiring significant fine-tuning to avoid conflicts in their sometimes-competing objectives.

What we have created is an integrated (read: ‘all-in-one’) planning and control framework that performs all of the functions of existing safety systems, in addition to predictively avoiding future hazards. This framework uses a fundamentally-new and incredibly-useful threat assessment method to predict the danger or ‘threat’ posed to the vehicle given its current state and the state of its surroundings. Based on this threat assessment, it then determines when, how, and to what degree it must intervene to ensure that the vehicle does not crash, lose control, or otherwise endanger its occupants. The controller is designed to allow the human driver as much control as possible in low threat scenarios and intervene only as necessary to keep the vehicle safe in high-threat scenarios. In the figures and videos that follow, I’d like to demonstrate a subset of the framework’s capabilities using figures and videos selected from the thousands of simulations and over 800 experimental trials that weíve used to vet it. Note that due to proprietary controls at Ford’s proving grounds, we were unable to record video of our Jaguar S-Type performing these maneuvers. Instead, we recorded telemetry data from each experiment and re-produced the results in high-fidelity simulation software (ADAMS/car).

Each of the videos below overlays the results from two simulations: the gray vehicle is controlled solely by a human driver model whereas the blue vehicle is also fitted with the semi-autonomous controller. In experimental trials, 8 different human drivers, each with different driving styles, were tested.

### LANE-KEEPING

The experiments shown in the figure below illustrate the semi-autonomous controller’s ability to adjust its behavior to the preference and/or performance of the human driver. The upper plot shows the vehicle path as the driver drifted laterally in the lane (edges shown in gray). The lower subplot shows the proportion of available steering control assumed by the controller.

Note that by simply changing the threshold threat at which the controller intervenes, we can allow the human driver more or less control in low-threat scenarios (between X = 0 and 100 meters) without adversely affecting the controller’s ability to keep the vehicle safely within the lane in high-threat situations. Thus, an inexperienced or cautious driver might prefer more controller intervention all the time in order to smooth out mistakes, while a seasoned or more adventurous driver would prefer that the controller not intervene until this intervention was absolutely necessary. In the figure above, the red solid line represents an intervention function tuned to the more cautious driver while the magenta dash-dotted line shows the results of tuning the controller to more experienced driver. Notice that in both cases, the controller allowed the human to wander freely within the lane, while intervening as necessary to prevent unsafe lane departure. The black dashed line shows what happens when the controller is turned off.

### HAZARD AVOIDANCE AND STABILTIY CONTROL

The video below demonstrates the navigation framework’s performance in the presence of stationary hazards such as road edges, roadway obstacles (not shown), etc. In this simulation, the driver of both vehicles actively seeks to remain on the road surface — a difficult feat at 20 m/s (~44 mph).

Notice that including the semi-autonomous controller in the control loop not only keeps the vehicle stable, but also moderates the driver’s inputs in the process. Whereas the unassisted driver oversteers and loses control of the vehicle, the assisted driver notices that the vehicle is responding as desired and is thus more moderate in his steer commands. This allows him to maintain control of the vehicle. Moreover, allocating less than 50% of the available control authority to the controller (see green bar on the right) is sufficient to keep the vehicle on the navigable roadway and within 0.4 meters of the (invisible) line on the center of the roadway that the driver model is trying to track. The combined effect of both inputs (driver and controller) is a vehicle trajectory that more closely tracks the path the driver is trying to follow than the driver could accomplish on his own.

In scenarios where a drowsy, inattentive, or otherwise-impaired driver fails to steer around an impending threat, the semi-autonomous controller foresees the threat, gauges the control action necessary to avoid it, and if the driver does not respond appropriately, takes the necessary control to keep the vehicle safe. Once the threat has been reduced, it returns control to the driver. The video below demonstrates one such case.

In order to avoid moving hazards, the semi-autonomous framework predicts their future position and pre-emptively assists the driver in avoiding those regions of the environment. In both of the videos below, the human driver acts as though he doesnít see the vehicles up ahead (no steering input). In the first video, the controller recognizes that a passing opportunity is available and takes only as much control as necessary to execute that maneuver. The second video illustrates a slightly different case in which the yellow vehicle accelerates once the blue vehicle initiates a passing maneuver (weíve all known one). In this case, the controller behaves much like an alert driver would ñ seeking first to pass, then pulling back in behind the yellow vehicle as it accelerates.

### CHALLENGES AND FUTURE WORK

I hope that the ideas discussed in this mini-series have provided a glimpse into the unique challenges and opportunities facing the emerging science of semi-autonomous control. While the issues and potential solutions weíve discussed in these four articles might seem a bit long-winded for a blog, they only scratch the surface of the technology, user studies, and legal infrastructure requirements that must be satisfied before these systems can be commercially implemented. Not the least of these considerations are driver acceptance issues. Almost everywhere I go to present this technology, one of the first questions I am asked is whether our system will come with an ‘OFF’ switch. Many people distrust the invisible face of automation and prefer to feel like they are in complete control. While we cannot completely concede the latter without sacrificing safety, we can certainly improve drivers’ perception and acceptance of autonomy by creating reliable, non-intrusive systems that modify driver inputs as little as possible while avoiding hazards. Significant work remains to be conducted in both human factors and usability studies before this research is road ready (my standard legal disclaimer), but I believe that at some time in the near future, it will be. Here’s to smaller, lighter, safer, and more efficient automobiles!

### ACKNOWLEDGEMENT AND INVITATION

I’d like to thank Dr. James Allison for his invitation to contribute these articles. Writing them has been an exercise in making my research more understandable to non-technical readers. For those of you who would like more details (and believe me there are many), I would invite you to read any of the applicable papers/theses listed on my website. If you have further questions, or would like to continue the conversation offline, I would be more than happy to visit with you. Please feel free to send me an email and/or leave comments below.

Posted: February 12th, 2011 | Filed under: Design, Modeling, Sustainability, Transportation | No Comments »

# Part III: Fundamental Challenges Facing Existing ADAS Systems and Description of The Alternative

Sterling Anderson, a Ph.D. candidate at MIT, continues his discussion of semi-autonomous driver assistance systems in this post. If you haven’t read his previous posts (Part 1, Part 2) you might want to start with them.

In my last article, I described existing driver assistance systems and introduced the notion of threat assessment. Near the end of the post, I described how the choice of threat assessment metrics is critical to the performance of any advanced driver assistance or semi-autonomous system. This article explains how existing systems assess threat and describes two significant problems caused by their approach. It then describes our alternative approach to threat assessment and semi-autonomous control. My next and final post will then demonstrate our system’s performance in both simulated and experimental results.

Significant Challenges Facing the Current State-of-the-Art

The first challenge facing existing systems is their inability to accurately capture the threat inherent to a particular scenario in a meaningful way. Because assimilating multiple sources of threat into a single, actionable metric is difficult, the majority of ADAS systems in existence today consider only one hazard at a time and limit their assistance to one dimension or another. For example, adaptive cruise controllers focus on the closest obstacle within the ego vehicle’s line of sight (longitudinal dimension) and apply the brakes as necessary to avoid rear-end collisions. Lane keeping controllers focus instead on the lateral dimension, neglecting longitudinal vehicle dynamics and collision hazards in order to maintain a desired position and/or heading within the lane. Like the child playing checkers for the first time, the tactical, near-term focus of these systems often fails to consider the effect of current evasive actions on future threat scenarios. For example, Figure 1 illustrates the predictions of an emergency braking system (solid green), along with those of a lateral collision avoidance controller (dashed blue). Without considering Obstacle B or the maneuver required to straighten out after it has passed Obstacle C, the obstacle avoidance system might consider a pass to the left of Obstacle C preferable to an emergency braking maneuver. Such a decision could be disastrous if either 1) Vehicle A cannot straighten out in time to avoid running off the road or 2) Vehicle A cannot avoid an oncoming Vehicle B once it has passed Vehicle C.

Figure 1. Illustration of tactical avoidance maneuvers considered by existing driver assistance systems

The second major problem facing existing driver assistance systems is closely related to the first: the myopic focus of existing systems on a single source of threat requires that a vehicle be equipped with multiple safety systems. For example, comprehensive assistance might require that the same vehicle be equipped with warning devices to alert the driver, anti-lock brakes to prevent skidding, yaw stability control to prevent loss of control, adaptive cruise control to prevent rear-ending the vehicle ahead, lane-keeping control to prevent wandering out of one’s lane, etc. While each of these systems may perform adequately by itself, their combined output when placed together on the same vehicle can lead to vehicle performance that is suboptimal at best and unpredictable or inconsistent at worst.

For instance, imagine a vehicle traveling quickly along a curve in road (illustrated in Figure 2). Wanting to avoid the large truck that is passing you on the inside of the turn, you drift toward the outer edge of your lane. As the lane departure warning begins to sound (adding to your already-heightened apprehension), the lane assist system takes control of the steering wheel, turning it hard left. As you swerve left, the car begins to roll right, whereupon the rollover alarm lights begin to flash and the roll stability controller engages, turning you back toward the outer edge of the road and again engaging the lane keeping controller. You can imagine how this scenario might continue. The point is that when distinct systems with disparate goals try to control the same vehicle, conflicts in their warnings and their steering, acceleration, and braking commands are bound to result. Figure 3 shows a (simplistic) schematic of how multiple systems arranged on the same vehicle can sometimes interact.

Figure 2. Illustration of a passing maneuver (see text for narrative/context)

Figure 3: Illustration showing how using multiple heterogeneous driver assistance systems in the same vehicle can have detrimental consequences.

The Alternative: An Integrated, Semi-Autonomous Active Safety Framework

To summarize the above, existing active safety systems are both incomplete in their assessment of threat and ineffective at sharing control with the human driver. This is where we come in.

When I began working with Karl Iagnemma and Steve Peters at MIT’s Robotic Mobility Group a couple of years ago, we began exploring a promising new idea for semi-autonomous control. We based our system on the simple observation that most human drivers tend to operate within a field of safe travel as opposed to along a predetermined path. Thus, instead of starting with a simple avoidance path, we chose to define a corridor through the environment that avoids obstacles, road edges, and other hazards. Then rather than selectively replace the driver when s/he strayed from the automation-desired path, we chose to gradually blend the driver’s inputs with the controller’s – giving the driver free reign while s/he remained within the safe corridor and only intervening as the likelihood of his or her leaving that safe corridor or losing control, increased.

The framework we developed (illustrated in Figure 4) storyboards as follows:

As a human driver navigates the vehicle, forward-looking sensors detect road edges, identify and terrain features (slopes, holes, etc.), and localize obstacles. Based on this information, a safe corridor that avoids these hazards is defined. A mathematical model of the vehicle is then forward-simulated to determine the safest or most stable path through the corridor given the vehicle’s current state (position, velocity, roll angle, etc.) and the current state of the environment. Because this optimal trajectory can be considered the best maneuver that can possibly be performed given the current circumstance, it is then used to assess the (best case) threat posed to the vehicle. In other words, no matter how skilled the human driver is, s/he will be unable to perform any better than the “best case” avoidance maneuver. As this “best case” maneuver becomes more dangerous, so does any maneuver that the human might attempt. In low-threat scenarios, the human retains full control of the vehicle (K=0 for you sharp-eyed readers). As threat increases owing, for example, to the driver’s failing to make adequate preparations to avoid an obstacle, so does the level of control authority given to the autonomous controller (gain K in the Figure 4 below). In extreme scenarios, when the avoidance maneuver required to keep the vehicle safe becomes so severe that only an optimal maneuver can be expected to pull it off, gain K becomes one and the system effectively acts as an autonomous controller until threat is reduced to human-manageable levels.

Figure 4: Block diagram illustrating basic framework operation

Figure 5 illustrates what the predicted avoidance maneuver might look when the vehicle is at position 1 (low threat, full human control), as well as how that prediction might appear when the vehicle reaches position 2 (high threat, nearly-autonomous operation).

Figure 5. Illustration of how corridor boundaries affect the optimal trajectory prediction and how the threat assessment based on that prediction affects the control allocation (level of autonomy)

The video below illustrates the semi-autonomous controller in action. Corridor constraints are shown in black and green, with the vehicle’s trail in blue and the predicted trajectory in red. In this simulation, the human driver fails to see or respond to a hazard. As the system’s prediction begins to predict a more and more severe avoidance maneuver, it gradually asserts only enough control necessary to avoid the hazard before giving control back to the human driver.

To conclude, the system we’ve developed combines the environment’s many hazards into a single, safe corridor and moderates the driver’s steering and braking inputs as necessary to keep the vehicle within that corridor. In low threat scenarios, the driver maintains full control of the vehicle. As threat increases, the controller shares control with the human driver to ensure that the vehicle does not leave the safe corridor. By inherently considering multiple sources of threat in a single, unified framework, this approach provides a significant advantage over existing driver assistance systems.

In my next post, I’ll show how this framework performed in the over 800 experimental trials with 8 different human drivers on a Jaguar S-Type. Stay tuned.

Posted: February 2nd, 2011 | Filed under: Design, Optimization, Transportation | 2 Comments »

# Part II: Driver Assistance Systems and the need for Threat Assessment

Please welcome back Sterling Anderson, a continuing guest blogger and Ph.D. student at MIT working in the Robotic Mobility Group. In today’s article, Sterling tells us about further developments regarding his work in semi-autonomous vehicle control. In his first post, Sterling discussed how this work relates to sustainability by helping reduce the vehicle mass required to keep its occupants safe. Here he describes an important aspect of these systems: threat assessment.

Hello again! With a few patents in the pipeline, and having given you a chance to think about what a semi-autonomous hazard avoidance system might look like, we’re ready to discuss the system we’ve developed. In this post, I briefly (re)introduce the vehicle safety problem and describe the current state of the art. I then highlight significant challenges inherent to the design of a comprehensive driver assistance system and describe the need for improved threat assessment techniques. In my next post (Part III of this series), I will point out two fundamental challenges facing existing ADAS systems and introduce our approach. Finally, Part IV will demonstrate the performance of our system and outline the challenges it faces going forward. Each part in this series will close with a thought question for the interested reader, which you are free to discuss either in the comments section below.

### THE PROBLEM

Vehicular safety is a problem that, I think, needs little motivation. Recent traffic safety reports from the National Highway Traffic and Safety Administration show that in 2008 alone, over 37,000 people were killed and another 2.3 million injured in motor vehicle accidents in the United States. While the longstanding presence of collision mitigation systems (seat belts, roll cages, crumple zones, etc.) has contributed to a decline in these numbers from previous years, it has failed to eliminate collisions altogether and has limited engineers’ ability to create smaller, lighter, and more energy-efficient vehicles. This is where Advanced Driver Assistance Systems (ADAS) – systems designed to avoid hazards altogether – come in.

### TYPES OF ADVANCED DRIVER ASSISTANCE

Advanced Driver Assistance Systems (ADAS) in use today can be (roughly) placed into four classes. The first of these classes can be said to be perhaps the most hands-off of the driver assistance techniques. Systems that fall into this class are broadly known as driver-warning systems and include lane departure warning systems, lane change assistance systems, and collision warning systems (sometimes imprecisely referred to as “collision avoidance systems”), among others. Driver warning systems typically provide feedback to the driver on visual, audible, or in some cases haptic (touch) channels. Research in this area is both active and complex owing largely to the immense variability that exists between human drivers and the consequent difficulty of predicting exactly how each will respond to various warning cues. For example, where a flashing light or audible tone may be helpful to some, it may unnerve, confuse, or even annoy others.

Stepping up the level of autonomy (or “hands-on’ness”) a notch brings us to collision preparation systems. This class of ADAS seeks to prepare for or help the driver avoid accidents. Examples include systems that pretension the seatbelts, prime the brakes, reduce the speed, or even adjust the suspension stiffness when a collision or threat appears imminent or when various sensors on the vehicle sense excessive wheel skid or roll angle.

One step closer to full vehicle autonomy lie Electronic stability control (ESC) systems, which help the driver avoid skidding and loss of control by selectively applying the brakes. This incredibly-useful (and increasingly-popular) ADAS class includes anti-lock brakes, yaw stability controllers, and roll stability controllers, among others.

Still greater levels of vehicle autonomy are found in what can be called “semi-autonomous” systems. The primary difference between these systems and the more passive stability control systems just described is that semi-autonomous hazard avoidance systems actively determine a course of action that may differ from the driver’s intended maneuver and may, when necessary, cause the vehicle to deviate from the course or speed that the driver’s commands prescribe. For example, the adaptive cruise control systems that since their 2006 debut in the United States have become an icon for intelligent (and expensive) high-end vehicles, determine based on the relative speed between the host vehicle (itself) and a hazard vehicle (the guy in front of you) whether, when, and how much to adjust velocity to avoid a collision. Another semi-autonomous ADAS that exists in very limited form today is the lane-keeping system. As its name implies, a lane-keeping system actively seeks to keep the vehicle within its current lane by applying anything from steering torque overlays to differential braking commands.

At the far end of the vehicle autonomy spectrum lie the autonomous systems. The more technical/nerdy among you may have heard or read about these during the DARPA Urban Challenge, and DARPA Grand Challenge competitions. Or, you might remember Nightrider. Autonomous systems are designed to navigate a vehicle without any human input. The common approach is to plan a path through the environment given sensory information about the location and velocity of obstacles, then track that path using a suitable controller.

ADAS system classes arranged in order of increasing vehicle autonomy

### THREAT ASSESSMENT

At the heart of each of these sytems lies the need to determine, based on the current state of the vehicle, the environment, and (optionally) the driver, the level of threat the current situation poses to the vehicle. The algorithms used to make this evaluation are known as threat assessors and can be argued to be perhaps the single most important component of any active driver assistance system. Without an accurate assessment of threat, these systems can be ineffective at best (imagine a collision warning sound incessantly and unnecessarily dinging as you drive) and downright deadly at worst (as when a lane keeping controller misreads a lane marking and sends the vehicle careening off the road). Figure 1 illustrates the relationship between threat assessors and ADAS systems.

Figure 2. Venn diagram illustrating the relationship between threat assessment and various modes of driver assistance.

Given the paramount importance of an accurate threat assessment, it becomes the task of any designer to determine what exactly constitutes “threat” and how to combine various sources of threat in some meaningful way. For example, imagine a vehicle traveling down an urban road amidst other vehicles. Potentially, any vehicle up ahead could pose a threat to the host vehicle were it to slow, stop, or lose control. Similar arguments can be made for other obstacles such as pedestrians, bikers, pets, curbs, etc. How, then, does one evaluate the threat each of these obstacles poses to the vehicle? Furthermore, how does s/he combine the threat posed by various (and often very distinct) sources into a single metric upon which s/he can base the decision as to how to best assist the driver? Further complicating this task is the requirement that not only must the vehicle avoid colliding with obstacles, but it must also stay within its own lane (or whichever lane the driver chooses), remain on the road, and avoid skidding, rolling, or otherwise losing control.

Though these questions may sound philosophical, they have significant practical implications for an intelligent assistance system, which must determine whether, when, to what degree, and in exactly what manner to intervene to help the driver simultaneously avoid collisions, instability (skidding, rollover, etc.), and loss of control. In my next post, I will discuss how existing ADAS systems assess threat and seek to assist the human driver and describe how these approaches fall short of the ultimate goal of comprehensive advanced driver assistance. I will then describe the alternative: our threat assessment and semi-autonomous control system.

Posted: September 29th, 2010 | Filed under: Design, Transportation | 3 Comments »

# 18 Minutes

Guest blogger Greg Kushmerek continues his series of articles on bike commuting:

I am the parent of young children, one of which has recently started at public school. As with many working parents, I need to juggle my work schedule with their school schedules.

This week I tried a little experiment: compare a car-based vs. bike-based commute while doing school drop-off for a 15-mile commute.

#### Bike Day

• Put bike seat on back of bike.
• Ride to school (5 blocks).
• Lock bike to fence (no bike racks).
• Drop-off
• Ride back home and deposit bike seat.
• Bike to work

Bicycle: Dahon Cadenza (my slower, heavier bike). Arrive at work: 9:28

I was showered and working before 10AM. Had the work VPN been up that morning, I’d have had 30 extra minutes at home to check and respond to messages.

#### Drive Day

• Walk child to school
• Drop-off
• Walk back home
• Drive car

Car commute conditions were a touch heavier than normal, but within the mean. Arrive at work: 9:10

#### Conclusion

Sacrificing the bike for the car (or forsaking the bike for the car — your call) doesn’t yield major gains, at least not for me. Had it been inclement weather or a good deal colder the car puts in a more obvious advantage. The time gains from the car are even a bit inflated when you consider that on car-day, I arrived at the school five minutes earlier. One could argue that less traffic would lead to bigger time gains, but I know I can get to work faster on my Felt (about 3mph / 5min faster).

Given a flexible work environment where some on-line work or staying a touch later is allowed, the time difference isn’t enough to warrant giving up the bike for the car.

For some people arriving 18 minutes later and sweaty is just a no-go. For those who live a lot closer to where they work, that time gap could practically vanish, and one might not even need to clean up.

Posted: September 18th, 2009 | Filed under: Cycling, Transportation | 4 Comments »

# Extreme Efficiency: Secrets Behind Miserly Solar Cars Part III (Tires)

Solar cars designed specifically to race in competitions such as the North American Solar Challenge or the Green Global Challenge (previously the World Solar Challenge) must somehow move at freeway speeds with less power than a typical hairdryer. Building these vehicles is a grand exercise in energy efficient design, and demonstrates what’s possible when engineers focus on producing maximum results with very limited power consumption. This is the third installment in a series that discusses several strategies solar car designers use to squeeze the most performance out of a vehicle-sized solar array. As with the larger energy system that powers our homes, vehicles, and factories, the best solution is not necessarily to focus only on producing more (ideally renewable) power, but also to identify and eliminate waste in systems that use energy. Designing for energy efficiency is a very cost-effective strategy to addressing energy problems.

One significant source of energy consumption in cars is tire rolling resistance. Basically, it takes some amount of force to roll a tire forward, even if you are not accelerating or going uphill. A simplified model of rolling resistance is:

$F_r=C_{rr}F_n$

This equation describes how much force is required to roll a tire forward at a constant speed on a flat road; this force is called rolling resistance $F_r$. It depends on two things: the vertical load supported by the tire (i.e., the normal force $F_n$), and the coefficient of rolling resistance $C_{rr}$. The normal force depends on how weight is distributed in your car, and the rolling resistance is a function of tire design (and is also influenced by things like temperature, speed, and tire slip). Rolling resistance goes up proportionately with both normal force and with $C_{rr}$. We would like to reduce rolling resistance in order to reduce the energy consumed while driving. It’s easy to see that one way to do this is to reduce how much a car weighs, which reduces $F_n$. Suppose we’ve eliminated as much vehicle mass as possible, and still want to reduce rolling resistance further. How do we reduce $C_{rr}$? To understand this, let’s have a look at where rolling resistance comes from.

When rubber tires roll over the road they deform. The spot that touches the road (the contact patch) is flattened just a little due to the force of the car pushing down. Imagine what happens to one piece of rubber in your tire as the tire rolls on the ground. Looking at the drawing below, at position 1, the piece of rubber is slightly curved. As the tire rolls, the piece of rubber moves into position 2, and it starts to deform. By the time it gets to position 3, it’s pretty much flat, and then as it moves through position 4 to position 5, it returns to its original shape.

All the rubber in the tire tread and sidewalls goes through some type of deformation with each revolution of the tire. It takes energy to deform rubber. We get most of that energy back when the rubber ’springs’ back into shape. But rubber is not exactly like a spring; you don’t get back all the energy you put into it. Rubber is what we call viscoelastic. The elastic part of viscoelastic is the springy part. Something has elastic behavior if it springs back into shape after being deformed. The viscous part means that when something is deformed, energy is lost, and resistance to deformation increases with how fast you try to deform it. Think of stirring a pot of honey; if you stir it slowly it doesn’t take much effort, but if you try to stir it fast the viscosity of the honey makes it harder to stir. Where does all the energy go from stirring? The honey doesn’t ’spring back’, so you can’t recover the energy like you can with a spring. The energy from stirring was converted to heat; the honey became a little bit warmer.

Tires exhibit both viscous and elastic behavior. Some of the energy is recovered when the rubber springs back into shape after rolling through the contact patch (point 3). Due to the viscous nature of rubber, there is extra resistance to deformation, as well as resistance to returning to its normal shape. The energy used to overcome this extra resistance is converted to heat; bending rubber back and forth makes it heat up (sort of like stirring the honey). Have you ever noticed how tires get warm after driving? The energy that warms your tires is energy lost. How can we minimize this lost energy (and reduce rolling resistance)? There are three main approaches:

• Reduce tire deformation: if tire rubber is deformed less, then less energy will be consumed. This can be accomplished by increasing tire pressure (one important reason to make sure your tires are inflated properly). It’s important not to over-inflate tires, however, as this could degrade handling and ride quality, compromise safety, and accelerate tire wear. Tire deformation can also be reduced by adjusting tire design, that is, changing its shape and what it’s made of.
• Reduce how much tire is deformed: narrower tires and tires with thinner tread have less rubber that moves in and out of the contact patch, reducing how much energy is lost from tire deformation. There are tradeoffs, however. Narrow tires may not handle as well, and thinner tread reduces durability.
• Reduce rubber ‘viscosity’: using a harder rubber compound can help shift tire behavior closer to purely elastic, meaning that a greater proportion of energy that goes into deforming rubber is elastically recovered. Again, there is a tradeoff. Harder rubber compounds may not grip the road as well as softer compounds.

Some tire manufactures have created tire specifically for solar cars, with emphasis on ultra-low rolling resistance. Solar car tires are thin, high-pressure tires with hard rubber compounds. They have rolling resistance coefficients as low as 0.0025, whereas high efficiency passenger car tires have coefficients near 0.006, and typical passenger car and light truck tires have coefficients much higher than that. To give you a sense of the legendary efficiency of solar car tires, I was contacted by engineers interested in using solar car tires on bicycles they were developing for breaking human-powered speed records. Solar car tires are more efficient than racing bicycle tires.

Below is a photo of a solar car tire along with a view of the suspension (this is a photo of the Stanford solar car from several years ago). Notice the electric hub motor just to the right of the wheel. There is a direct connection between electric motor and wheel; no drive shafts, gears, belts or chains to sap energy.

The next photo shows a pile of solar car tires. Since these tires are optimized for energy efficiency, they don’t last very long. They must be replaced frequently, and it takes a large pile of tires to make it through a long cross-country solar car race.

Solar car tires are intended for specialized racing vehicles, and are obviously impractical for passenger vehicle applications. Nevertheless, we can take lessons from their design to help improve efficiency of production vehicles. Maybe we could move toward higher pressure tires, and use more advanced suspension design to help counteract the harsher ride from stiffer, high pressure tires. As we make other vehicle aspects more efficient (such as aerodynamics or powertrain design), the energy lost through rolling resistance will become an increasingly important factor, and is an opportunity for improvement.

Posted: August 11th, 2009 | Filed under: Design, Energy, Sustainability, Transportation | 5 Comments »

# Practical Implications

Guest blogger Greg Kushmerek continues his series of articles on bike commuting:

Someone I like to follow on-line is Andy Kessler. He’s a financial journalist and author of sorts who’s written a few books and used to run a hedge fund. He’s very practical, has an excellent sense of the arbitrary nature of “value”, and I’ve come to realize as I’ve followed him that he seems to hate cyclists, or at least cycling evangelists.

Andy’s made a few comments denigrating, really almost fearing, a vision of the future where cars are not dominant and cyclists such as me have “won”. He has a real distaste of a future where everyone lives in densely populated areas in order to make a greener place. If I’ve read him correctly, he thinks that would attack the very nature of what it means to be American — that having onerous burdens that force people to live in cities would create a society devoid of innovation.

There are some great areas for discussion in that position, the first and foremost being that it’s a real concern shared by many people who would rather not have to face arriving at work sweaty from a summer ride with dirty hands from trying to rebuild a snapped chain. That little saddle doesn’t sound as appealing as a cushy leather seat and air conditioning (or heat in winter). How do you reach these people? If you can’t, are you going to force them towards that vision via government regulation?

Now Andy has a sharp wit and it’s very tempting to point out that the proper market-oriented answer is that rich people like him would ultimately pay teams of people like me to come out to his vast estate and cart him around in our little cycling paradise. The ruling class could grow to a race of Jaba-The-Hut proportions and lord it over the rest of us.

However it’s at this point I think one should step back and look at this from a more practical standpoint: could the cycling vision really work in America? I’ve previously mentioned that The Netherlands only oriented itself to cycling in the 1960s, which implies that with enough will the same kind of thing can happen elsewhere. The problem with that position is that it ignores what 40 years of infrastructure development in the USA have created: a population spread out over a wide area, much bigger than tiny Holland.

Think about it: if the Feds suddenly put out bike friendly infrastructure and created an economic environment more favorable to cycling, what would it mean? My city condo would go up in value as some people would find my dense neighborhood more attractive, but plenty of people in the suburbs and exurbs would neither want to move or appreciate a reduction in their own property values. The houses in far flung places won’t magically disappear and the communities won’t just transplant themselves. Some people hate dense areas and generations have grown up in spacious suburbs. People will still live in places with long roads in between their destinations. They’ll start driving more efficient four-wheeled vehicles before they move. You don’t have to be especially bright to see the real implications.

In this kind of environment, what do you do to make cycling more attractive in suburban areas? Bike lanes are ridiculous on most roads since they’re plenty wide enough. It’s the main connecting roads that you have to think about. Should there be bike lanes on those? Should there be more “bike stops” so people can duck out of the rain? One policy I like, that would make some people howl, is to reduce the percentage of car use on those main arterial roads by 40%. Shove cars in narrower lanes in the middle, put up some raised granite separators on the outside, and make the space from the granite to the side of the road exclusive to cyclists and mopeds.

Maybe you could buy them out? The Great Smokies National Park used to be plots of private farmland until the 1930’s. We spent the 60’s using eminent domain to raze the center of cities and put in highways. Should we now use those same policies to reverse engineer what’s in place?

In the end, this only confirms Andy’s fear. The houses won’t get any closer if you force the roads to be more cycling friendly. Not everyone will leave and those who do will not do it all at once. Some business will not want to expand into suburban areas if the government creates market conditions more favorable to denser cities or exurbs. While I think that this kind of environment won’t stifle innovation — it will enhance it as people rush to fill voids that a new market condition creates — that’s cold comfort to Andy’s point of view.

Still, I am vexed over what to do with all of those suburbs. You can’t simply say “too bad”. The political backlash would kill pro-cycling integration policies if those policies became onerous to suburban living. Yes you can create market conditions that encourage people to give that existence up, but you need to think of ways to accommodate those who do not switch. After all, even the most avid suburban cyclist is likely to have a car for errands.

I’d like to know what you think?

Posted: August 5th, 2009 | Filed under: Cycling, Policy, Transportation | No Comments »

# Uniform Policies Are Key

Guest blogger Greg Kushmerek continues his series of articles on bike commuting:

A key design tip in the world of print is consistency: keep consistent design elements in place. People recognize a designed page as “belonging” to the overall product. Apply this to the physical world and you get predictability, and that’s good for something like traffic management.

We don’t have enough predictability on today’s roads, however, for drivers or cyclists. For example: should bikes be subject to all rules of the road, or should they have their own set? Aside from the fact that some cyclists create their own rules, I have seen plenty of examples where bikes have been given the right to do things that cars cannot.

For example: it’s the law in Massachusetts that bikes can pass cars on the right so long as the local town or city hasn’t explicitly outlawed the practice. Pass someone on the right in a car, and you’re subject to a ticket (it was the first one I ever got when I was a teen). Of course, few people know this — I once had someone try to use his car as a rolling roadblock to prevent me from going down the right side.

More signage would help as would other aids to navigation. Intersections can have bike traffic lights or bike signs explicitly dictating what cyclists may do. Signs in advance of major intersections could warn cyclists and drivers that the road is about the change and thus the dynamics are about to be different.

Consider bike lanes again: the non-uniformity of how they appear, how long they last, on what kinds of roads you’ll see them all lead to ambiguity. Ambiguity in traffic is bad. When are they solid lines? When are they dashed lines that allow cars in them? And, as I previously mentioned, how close to the side of the road are they? I personally prefer the idea of redefining the idea of a road to be partly a place where cars travel and then partly a place where “other things” happen. Some roads are generously wide enough that you can cut out eight feet from the side, leave six feet for parking, a foot of space, and the last foot be available for bike lanes. Consistent marking would make it clear where moving cars do and do not belong, and people would form new habits.

What about sidewalks? Any cyclist who’s spent enough time on the road has been asked, rhetorically, “Why don’t you go back on the sidewalk where you belong?” (Presumably, the thought of a cyclist rapidly sneaking up on a baby stroller is more appealing to these drivers than having to share the road.) Just when is it a good time to be on a sidewalk? Ever? Never? I think they’re even more dangerous for cyclists than most roads, but the laws here are also quite mixed even encouraging cyclists to use sidewalks.

I think it’s time to consider a federal-level set of guidelines tied to highway and road funding. Signage, lane width, location, requirements on which kinds of roads should have bike lanes, consistent rules — all of this can come right down and level the playing field to create the predictability we need on the roads. It won’t stop cars from complaining about bikes on the roads, but hopefully it will move their complaints over how someone is biking on the roads and not whether someone should be biking on the road.

Posted: July 22nd, 2009 | Filed under: Cycling, Policy, Transportation | No Comments »

# Soaring Fuel Prices: Bane or Boon?

Last summer Americans had a taste of high fuel prices, and our driving habits and vehicle choices actually started to change. Substantially higher prices certainly would cause a lot of pain, particularly in the short term, but what benefits might we realize? Chris Steiner, Forbes columnist and author of \$20 Per Gallon: How the Inevitable Rise in the Price of Gasoline Will Change Our Lives for the Better, explained in a recent NPR interview that dramatically higher prices could lead to a better way of living.

Steiner predicts that as fuel prices climb, we will become less of a disposable society, and migrate to denser, more interactive living arrangements. Air travel may not be economically viable for most of us, and travel by rail will grow in popularity (look at nations in Europe or Asia with high-speed rail infrastructure for examples). Other positive changes include more exercise in people-centered (as opposed to car-centered) communities, cleaner air, better (local) food, and improved health. And let’s not forget one of my favorite impacts: increased popularity of cycling.

In addition to environmental and health benefits, curbing petroleum consumption is a national security issue. This video features retired generals and others discussing a recent report from CNA that ‘explores the impact of America’s energy choices on our national security policies’. Vice Admiral Richard Truly, USN (Ret.), discussed the urgency of helping improve public knowledge about energy use, and the importance of resolving our energy situation. General Chuck Wald, USAF (Ret.), explained that Americans must realize that our energy situation is not going to take care of itself without us being a part of it. The link to national security alone could be motivation enough to take action.

The transition to higher fuel prices and lower consumption will certainly be painful, and hurt more for certain segments of the population than others. Should we wait for fuel prices to rise due to market forces and adapt then, or should we take some preemptive action to ease the transition? A phased-in fuel tax could be used to fund required infrastructure changes, as well as investments in technology that will enable us to enjoy a high standard of living on far less petroleum. Revenues could also be used to assist those struggling most with the transition to higher fuel prices. Instituting a U.S. fuel tax would funnel revenue into infrastructure and investments that benefit Americans, whereas waiting for market forces to drive up fuel prices will instead boost revenue for oil producers. Automakers actually support a fuel tax, hoping that it will stabilize fuel prices so they can invest in advanced technologies with more confidence in future demand for energy efficient vehicles. The main question here is not whether fuel prices will increase, but would we rather transition with foresight and a strategy, or just wait until we are forced into reacting. The former option would certainly be less painful, and would leave us in a much better position after the transition.

A strategic transition would require a substantial fuel tax (or a price floor), but this appears to be politically impossible right now. What do you think it would take for U.S. citizens to support an appropriate fuel tax?

Posted: July 17th, 2009 | Filed under: Energy, Policy, Sustainability, Transportation | 1 Comment »

Guest blogger Greg Kushmerek continues his series of articles on bike commuting:

There’s a lot of arguments out there about whether bike lanes are good or bad, and a lot of the arguments against them seem to come down to “They create more problems for cyclists than they solve”. Perhaps that’s an oversimplification, but it’s an opinion I agree with with when looking at many implementations of bike lanes in my own area.

Consider Boston. Boston really should be a great biking city. It’s not that small, has lots of parks, fairly wide roads, and isn’t all that hilly right in the city area. However, biking in the city feels risky. The few attempts to put in bike lanes have simply stunk. The first bike lane I’m aware of is behind Jamaica Pond on Perkins Street. There’s some parking between the curb and the bike lane, and then the parking lane ends and the bike lane takes over. What happens is this: people fill up all the parking spaces and then just park right over the bike lane when parking runs out.

Now you can point your fingers at the Boston Transportation Department or Boston Police Department and say that they should be out there doing more ticketing, but that ignores the larger point. The implementation stinks. The bike lane competes with parking in a highly desirable location. The bike lane could have been one foot further out, eating into the regular road. This would make it clearer that there’s a real lane there. The lane could be using different paint than the simple white lines that, everywhere else in the city, denotes the shoulder.

Worse still is that the placement of the bike lane puts cyclists in a zone of danger. People come in and out of that area with their cars all the time to go walk around the park (yes, they drive to the pond to go jogging, but I’m not going there today — at least Massachusetts has the 2nd lowest rate of obesity in the country today). In other words, the risk of a cyclist getting doored is pretty good.

Imagine if the federal government had a law giving states and municipalities the incentive to put in bike lanes only if those lanes had little boxes all alongside them that randomly punched out at passing cyclists? That’s kind of what’s happening today. If you are resurfacing a road, you can ask the feds to chip in on the cost, which they’ll gladly consider if you agree to spray on some bike lanes.

So here we are: you have local transportation departments taking the cash and laying down lines about as close to the side of the road as they can get it, regardless of the parking situation. In Cambridge, MA, this led to a cyclist’s death a couple of years ago when a female cyclist in her lane on Mass Ave was doored and fell to the ground in front of a passing bus.

I don’t think this automatically makes all bike lanes bad. I think that bike lanes are a really good thing when they’re done correctly. I point to The Netherlands as one such example of doing these things well frequently, but this time I don’t have to look so far. Right in Newton, on Beacon Street, the town effectively cut the road in half by making a shoulder out of what was an unofficial second lane. It’s not now considered a bona fide bike lane, but that’s how it’s frequently used by many commuters and college students. Parking is limited and where there is parking, a passing cyclist has enough space to get around the car and not be in traffic.

I’d like to see more of this, and I’d like to see the feds put in some guidelines on just how a bike lane gets implemented rather than having them simply hand over a check.

What do you think makes a successful bike lane? How can the policy be better?

Posted: July 8th, 2009 | Filed under: Cycling, Policy, Transportation | 2 Comments »

# Green FAIL: U-Haul’s Spurious Greenwash Promotes Increased CO2 Emissions

U-Haul is jumping on the low-carbon bandwagon by promoting their ‘clean gasoline’ moving trucks over ‘dirty diesel’, but this blatant corporate greenwash is endorsing choices that actually lead to increased carbon emissions. Have a look at a screenshot from this U-haul webpage:

U-haul is telling us here that using a gasoline-powered truck instead of a diesel truck would reduce CO2 emissions. This campaign is actually doing more harm than good. It may garner more business for U-Haul, but switching from a diesel truck to a gasoline U-Haul truck will actually increase carbon emissions. This advertising campaign will also lead to indirect carbon emission increases by perpetuating misconceptions about diesel. Let’s have a more careful look at the numbers used here.

The core of U-Haul’s claim of lower carbon emissions is that the amount of CO2 emitted by burning a gallon of diesel fuel (22.2 lb) is somewhat larger than the amount released by burning a gallon of gasoline (19.4 lb) [Data source: EPA]. So what is wrong with U-Haul’s claim? It is based on the (incorrect) assumption that diesel and gasoline trucks get the same fuel economy. What U-Haul doesn’t explain is that you can do a lot more work with a gallon of diesel than you can with a gallon of gasoline. Diesel fuel efficiency is typically 40% better than for gasoline engines. That’s a huge difference!

The better fuel economy is a result of two main factors: diesel has 11% higher volumetric energy density than gasoline, and the Diesel cycle allows for much higher compression ratios than the Otto cycle used in gasoline engines (which makes for a more efficient engine). In other words, diesel fuel has more energy packed into it per gallon (which is part of why it has higher carbon content), and diesel engines do a better job of converting that chemical energy into mechanical energy used to move the truck.

A valid comparison between gasoline and diesel trucks would be based on equal amounts of work, not equal mpg—this is what really matters to the customer. Consider a potential rental truck customer that has a set amount of stuff that needs to be moved from a specific start location to a specific end location. What we should be comparing is how much CO2 is emitted from a gasoline truck vs. a diesel truck for moving the same amount of stuff along the same route. If you account for the substantially better fuel economy, the diesel truck will emit far less CO2 than the gasoline truck. The U-Haul comparison assumes both diesel and gasoline trucks get 8 mpg. For this to happen, the customer would have to remove some of his load from the gasoline truck to improve its fuel economy so that it equals the fuel economy of the diesel truck (which is carrying the full load). In this case the gasoline truck is doing far less work, and is not moving all of the customer’s stuff. The U-Haul comparison strategy is unrealistic and deceptive. Readers who are unaware of diesel’s inherently better efficiency may be misled into believing that choosing U-Haul would actually reduce carbon emissions.

In the fine print above, U-Haul states that ‘actual gas mileage may vary’. The fact that actual gas mileage does vary, in favor of diesel by a large margin, destroys U-Haul’s claim that gasoline trucks are better with respect to carbon emissions. The ‘various reliable sources’ statement in the fine print should raise another red flag: U-Haul is not the least bit transparent about these distorted claims.

In the fine print you will also find links to articles about new regulations for diesel particulate, sulfur, and NOx emissions, which are unrelated to the comparison of CO2 emissions between diesel and gasoline. Ironically, these articles explain how diesel-powered vehicles will be improved significantly in the near future.

The statement in this advertisement that diesel trucks emit more carbon than gasoline trucks (per gallon) may be deceptive, but it is technically correct given all the stated (but unreasonable) assumptions. The running tally of ‘CO2 emissions kept out of the atmosphere by choosing U-Haul’, however, is flat-out wrong (flying in the face of the Truth in Advertising Act). Whoever did these calculations didn’t do their homework. Correct calculations would result in a negative value here; that is, switching to U-Haul would increase CO2 emissions.

On U-Haul’s Corporate Sustainability page you can read:

Avoid “dirty” diesel
Mile for mile, diesel trucks release more toxic air contaminants, cancer-causing soot, and smog-forming emissions than gasoline-powered trucks. Greenhouse gas emissions from a gallon of diesel are 15 percent higher than those from a gallon of gasoline. That’s why all U-Haul rental trucks use cleaner-burning unleaded fuel.

The first two sentences, taken independently, are technically correct. Diesels do emit more particulates and other toxic gasses than gasoline engines on a mileage basis (at least with current emission control standards). And as I explained before, a gallon of gasoline does emit more CO2 than a gallon of diesel (14.4% more). However, putting these two statements together without clarification may lead people to believe that diesel trucks emit more CO2 on a mileage basis than gasoline, which is an incorrect conclusion. I roll my eyes at the third sentence. If U-Haul truly was concerned about carbon emissions, they would have transitioned their fleet to more efficient diesel trucks.

The financial savings claim in this ad is also incorrect. Not only do diesel trucks burn less fuel, but diesel is now less expensive than gasoline on average in the U.S. (gasoline: \$2.691/gal, diesel: \$2.616/gal, Source: EIA, June 25, 2009).

U-Haul is working hard to perpetuate incorrect negative stereotypes about diesel fuel and engines. An anti-diesel campaign magnifies their negative impact by delaying diesel adoption in the U.S. market. Switching to diesel-powered passenger cars could be a practical near-term solution to reducing carbon emissions and reducing dependence on foreign oil. We need to encourage the U.S. market to embrace diesel, not shun it. I want to note that diesel-powered transportation is not a long-term solution; we need to develop (as quickly as possible) a fossil-fuel free transportation system as a long-term solution.

What do you think of U-Haul’s CO2 marketing campaign? Why don’t you let them know (1-800-789-3638), and see what they have to say about it. Or, you could let others know (like these folks).

As an aside, if you want the ultimate in ‘green’ moving options, and you are moving locally and are feeling athletic, perhaps you would be interested in getting a few friends together for a ‘bike move‘.

Posted: June 25th, 2009 | Filed under: Green FAIL, Transportation | 1 Comment »