Please welcome the newest Design Impact guest blogger: Sterling Anderson. Sterling is a Ph.D. student at MIT working in the Robotic Mobility Group. In today’s article, Sterling writes about his work in the next generation of vehicle stability and hazard avoidance control, and how it relates to vehicle sustainability.
Today I’d like to briefly discuss exciting new developments in a field not commonly associated with or considered a critical component of vehicle sustainability. That field is vehicle safety. The connection I’d like to draw between safety and sustainability goes as follows: no matter what its energy source (gas, hybrid, electric, etc), a vehicle may be made more efficient by removing or otherwise lightening its structural elements. Many of these elements, however, such as secure seat belt harnesses, large airbag systems, sturdy roll cages, and large crumple zones, cannot be removed without increasing the risk of injury to vehicle occupants in the event of a collision. This limits the degree to which vehicles can be made smaller (which reduces drag), and lighter (less mass) without forfeiting the structural protection provided by larger and more massive vehicles.
Enter driver assistance systems. In recent years, the historical focus on passenger safety in human-controlled motor vehicles has shifted from collision mitigation systems such as seat belts, airbags, roll cages, and crumple zones to collision avoidance systems, which include anti-lock brakes, yaw stability control, roll stability control, and traction control. Whereas collision mitigation systems seek to reduce the effects of collisions on passengers, active collision avoidance systems seek to prepare for and avoid accidents altogether. This accident avoidance reduces – and may one day eliminate – the additional mass and design constraints required by passive safety systems.
But while existing collision avoidance systems are effective at reducing accident frequency, they are still limited in one respect: their avoidance methods are fundamentally “reactive” in nature. In the majority of these systems, controller intervention is based solely on current vehicle conditions, and thus cannot anticipate and prepare for future threats. For example, an anti-lock brake system seeks to help the driver avoid accidents by more intelligently applying his intended braking command – it does not preview the road ahead and decide to apply the brakes of its own accord. Ditto with stability and traction controllers; neither preemptively seeks to avoid hazards – each simply responds to the driver’s command. Thus, a drowsy, distracted, or otherwise inattentive driver receives very little benefit from such a system as it does not engage until he begins his own evasive maneuver.
Recent developments in onboard sensing (cameras, radar, laser-based sensing, vehicle-to-vehicle communication, etc.) and drive-by-wire technology have facilitated the development of collision avoidance systems that use information about the vehicle’s surroundings, along with predictive computer models to determine the best course of action to avoid an accident. If needed, such systems intervene and share steering and/or braking control with the driver. These “predictive” systems generally attempt to honor driver intentions, opposing them only when doing otherwise would lead to a collision or loss of control. By constantly monitoring a vehicle’s surroundings and predicting a safe path through them, they may warn the driver and take control of the vehicle steering and/or braking to avoid accidents before it is too late. Much like a copilot or driving instructor, this controller intervention should strike a necessary balance between the level and frequency of intervention: not altering the driver’s steering and braking inputs “too much”, “too soon”, or “too often” while still guaranteeing that the vehicle avoid hazards independent of that driver input.
In my work with MIT’s Robotic Mobility Group, we are currently developing a predictive active safety system that predicts the “best-case” trajectory through the environment, assesses the threat this trajectory poses, and intervenes as necessary to avoid accidents. We’ve tested this system in both simulation and experiment with excellent results. As the patent is still pending, I’ll defer details until my next post. Until then, you can see a demonstration of its performance in a few simulation videos posted on the research page of my website. In the mean time, and before I’ve biased your creativity with our solution, please brainstorm your own possible solutions. We have the technology to identify hazardous conditions and help the driver avoid collisions. What would you think of driving a car with a system like this? How do we know when intervention is “too much” or “too soon”? Feel free to discuss these ideas with others via the comment section below.
MIT Robotic Mobility Group