Motion planning for autonomous vehicles with the inclusion of post-impact motions for minimising collision risk

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision-making process of severity minimisation motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics, and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modelling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research.

Automated summary

This paper proposes a framework that incorporates the post-impact motion of an impacted vehicle into the decision-making process of severity minimisation motion planning. Through simulations and analysis, the effects of vehicle model complexity and collision parameters sensitivity are revealed, highlighting the importance of considering vehicle dynamics models and collision models in further research.