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Predictive Threat Assessment via Reachability Analysis and Set Invariance Theory

We propose two model-based threat assessment methods for semi-autonomous vehicles, i.e., human-driven vehicles with autonomous driving capabilities. Based on information about the surrounding environment, we introduce a set of constraints on the vehicle states, which are satisfied under “safe” driving conditions. Then, we formulate the threat assessment problem as a constraint satisfaction problem. Vehicle and driver mathematical models are used to predict future constraint violation, indicating the possibility of accident or loss of vehicle control, hence, the need to assist the driver. The two proposed methods differ in the models used to predict vehicle motion within the surrounding environment. We demonstrate the proposed methods in a roadway departure application and validate them through experimental data.