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.
This paper addresses the control of a highly automated vehicle in a traffic scenario, where colliding with other traffic agents is unavoidable. Such a critical situation could be the result of a fault in the vehicle, late obstacle detection or the presence of an aggressive driver. We provide an approach that allows the vehicle's control system to choose the manoeuvre that is likely to lead to the least severe injuries to vehicle occupants.The approach involves the off-line solving of an optimal control problem to create a set of trajectories based on controlling the steering angle rate and the braking rate at the vehicle's limits. Occupant injury severity prediction, based on accident data with the focus on impact location, is used by a real-time collision control algorithm to choose a trajectory from the pre-computed optimal set. A simulation set-up is presented to illustrate the idea of the collision control algorithm in a simple scenario involving dynamic traffic agents.
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