A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.

Automated summary

This study proposes a data-driven motion planning method to minimize injury severity for vehicle occupants in unavoidable collisions, by establishing a metric that models the relationship between impact location and injury severity and including it in the cost function of a motion planning framework. The method considers vehicle dynamics and constraints through a precomputed trajectory library, allowing for efficient computation and accurate representation of the vehicle. Simulation results show that the trajectory associated with the minimum cost mitigates collision severity for occupants of passenger vehicles involved in the collision.