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Design Impact » Blog Archive » Part IV: Semi-Autonomous Control Framework: Present Performance and Future Work

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.


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.


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.


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.


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!


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 |

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