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Pre-crash Motion Planning for Autonomous Vehicles in Unavoidable Collision Scenarios

Time: Mon 2022-06-13 09.00

Location: F3, Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Machine Design

Doctoral student: Masoumeh Parseh , Mekatronik

Opponent: Professor Daniel Watzenig, Graz University of Technology

Supervisor: Professor Martin Törngren, Maskinkonstruktion (Avd.), Inbyggda styrsystem, Mekatronik; Docent Fredrik Asplund, Mekatronik, Inbyggda styrsystem


Full deployment of Autonomous Vehicles (AVs) on public roads is challenging for organizations in the automotive domain in terms of developing safety standards and methods while taking legacy assumptions related to having a human driver and increased complexity and complexity handling into account. Specifically, the safety of AVs in the presence of other road users must be guaranteed as far as possible for different traffic scenarios. Furthermore, unsafe situations might emerge due to uncertainty in the environment of an AV. These situations could arise due to the unexpected behaviors of others (e.g., an aggressive driver), late obstacle detection, and internal failures. Avoiding a collision with other vehicles may thus not always be possible regardless of the complexity of the planned emergency maneuver.

This thesis aims to address the problem of motion planning and control for AVs in these unique situations of unavoidable collisions. Several factors that are important in the problem formulation of a pre-crash motion planning problem for severity minimization are identified and addressed. As a result, a framework is developed that incorporates these factors and combines motion planning and control, vehicle dynamics, and accident analysis to mitigate collision risk, in particular, by reducing injury severity for vehicle occupants and increasing safety by changing the configuration of unavoidable collisions. 

This thesis tackles this problem by first proposing an algorithm that, in real-time, allows an AV to choose one action/trajectory, from a set of pre-computed trajectories, associated with the lowest injury severity for vehicle occupants. The method uses the trajectory library approach combined with numerical optimization and optimal control theories. The choice of this trajectory mainly relies upon a metric derived from accident data analysis that relates injury severity and impact location. By incorporating collision risk as a combination of collision severity and probability, the need for a configurable collision probability threshold that decides when a collision mitigation system should be activated is identified. This decision threshold balances the ability to reduce collision severity with the undesired increase in the likelihood of a collision taking place.

The studies included in this thesis show that different decision-making strategies involving decision thresholds for collision mitigation/reconfiguration systems can lead to statistically significant differences in the resulting collision severity. Furthermore, unobserved heterogeneity may arise through the introduction of these systems, e.g., due to slight variations in the parameters of the algorithms they employ. The problem of motion planning in unavoidable collisions is further extended by proposing a unified system that incorporates the risks of post-impact motions resulting from the original impact. The extended framework can be configured for different contexts by adjusting its cost function according to relevant post-impact risks. 

The result of this thesis aims to contribute to the field of motion planning in unavoidable collisions and to provide guidance for further improvement of road safety. Further research is required to fully explore this field and address the challenges of motion planning and control in unavoidable collision scenarios.