Engineers at the University of Waterloo have developed decision-making and motion-planning technology to limit injuries and damage when self-driving vehicles are involved in unavoidable crashes. A paper on their work is published in IEEE Transactions on Intelligent Transportation Systems.
A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.—Wang et al.
After recognizing that a collision of some kind is inevitable, the system works by analyzing all available options and choosing the course of action with the least serious outcome.
What can we do in order to minimize the consequences? That is our focus.—Amir Khajepour, a professor of mechanical and mechatronics engineering at the University of Waterloo
The first rule for the autonomous vehicle (AV) crash-mitigation technology is avoiding collisions with pedestrians.
From there, it weighs factors such as relative speeds, angles of collision and differences in mass and vehicle type to determine the best possible manoeuvre, such as braking or steering in one direction or another.
Dongpu Cao, also a mechanical and mechatronics engineering professor at Waterloo, said that the system considers the whole traffic environment perceived by the autonomous vehicle, including all the other vehicles and obstacles around it.
Khajepour, director of the Mechatronic Vehicle Systems Lab, said the system is needed because the popular idea that AVs of the future will completely eliminate crashes is a myth. Although safety should improve dramatically, he said, there are just too many uncertainties for self-driving vehicles to handle them all without some mishaps.
There are hundreds, thousands, of variables we have no control over. We are driving and all of a sudden there is black ice, for instance, or a boulder rolls down a mountain onto the road.—Amir Khajepour
AVs are capable of limiting damage when a crash is unavoidable because they always know what is happening around them via sensors, cameras and other sources, and routinely make tens and even hundreds of decisions per second based on that information.
The new system decides how an AV should respond in emergency situations based primarily on pre-defined mathematical calculations considering the severity of crash injuries and damage.
Researchers didn’t attempt to factor in extremely complex ethical questions, such as whether an AV should put the safety of its own occupants first, or weigh the well-being of all people in a crash equally.
But when carmakers and regulators eventually hammer out the ethical rules for self-driving vehicles, Khajepour said, the system framework is designed to integrate them.
H. Wang, Y. Huang, A. Khajepour, Y. Zhang, Y. Rasekhipour and D. Cao (2019) “Crash Mitigation in Motion Planning for Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 9, pp. 3313-3323 doi: 10.1109/TITS.2018.2873921