Risk Quantification for Automated Driving using Information from V2V Basic Safety Messages

Using data from V2V links along with onboard sensor data is recognized as a crucial step towards the safety and reliability of future automated driving. We address short-term trajectory planning for the ego vehicle. We propose a threat field model of the traffic surrounding the ego vehicle. Informally, the threat field indicates the possibility of collisions in the vehicle's vicinity. We study uncertainty in the threat field due to uncertainty in the positions and velocities of surrounding vehicles, which may be known to the ego CAV via basic safety messages. The cost of trajectories is defined by the expected threat exposure, and the risk of trajectories is quantified based on the variance in cost. Uncertainty quantification is studied using Monte Carlo sampling as well a perturbation-based approach. The main result of this paper is the observation that small localization errors and/or speed measurement errors can lead to large risks in planned trajectories.