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.
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.
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.
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 |