Motion Planning and Control of Automated Vehicles in Critical Situations
Time: Tue 2021-06-08 15.00
Subject area: Machine Design
Doctoral student: Lars Svensson , Mekatronik
Opponent: Professor Christian Gerdes, Stanford University
Supervisor: Professor Martin Törngren, Maskinkonstruktion (Avd.), Inbyggda styrsystem, Maskinkonstruktion, Mekatronik; Lei Feng, Maskinkonstruktion (Avd.); Anna Pernestål Brenden, Integrated Transport Research Lab, ITRL
The road traffic environment is inherently uncertain and unpredictable. An automated vehicle (AV) deployed in such an environment will eventually experience unforeseen critical situations, i.e., situations in which the probability of having an accident is rapidly increased compared to a nominal driving situation. Critical situations can occur for example due to internal faults or performance limitations of the AV, abrupt changes in operational conditions or unexpected behavior from other road users. In such critical situations, the first priority for vehicle motion control is to reduce the risk of imminent accident. If needed, the full physical capacity of the vehicle should be employed to accomplish this. These unique circumstances distinguish automated driving in critical situations from the nominal case.
This work aims to tackle the problem of motion planning and control in such critical situations. We determine a set of characteristics that signify the motion planning and control problem in critical situations, in relation to state of the art algorithms. Further, we incrementally develop a motion planning and control framework, tailored for the particular circumstances of critical situations. In its current form, the framework uses a combination of numerical optimization, trajectory rollout and constraint adaptation, to allow motion planning and control with respect to time-varying actuation capabilities, while realizing a range of behaviors to mitigate accident risk in a range of critical situations.
Results for the research work are generated by exposing the framework to several categories of critical situations in a combination of simulations and full scale vehicle tests. We present the following main findings: (1) Inclusion of risk levels of stopping locations at the local planning level generates satisfactory motion behavior in the evaluated critical situations, enabling a combined assessment of risk of the maneuver and of the stopping location. (2) Traction adaptive motion planning and control improves the capacity of autonomous vehicles to reduce accident risk in critical situations, both when adapting to deteriorated and when adapting to improved traction in a range of tested critical situations. (3) State of the art friction estimation algorithms are insufficient for traction adaptive motion planning in terms of combined requirements on accuracy, availability and foresight. However, fusion of multiple estimation paradigms show potential to yield near-optimal performance.
The combined contributions of this thesis are intended as a step towards further improving accident avoidance performance of automated vehicles and driver assistance systems in critical situations. However, much research work remains to be done in this field. We emphasize the need for further research efforts in terms of experimentally evaluating the impact of motion planning and control concepts on accident avoidance performance in critical situations.