Validating the safety of self-driving vehicles requires an enormous amount of testing. By applying formal verification methods, we can prove the correctness of the vehicles' behavior, which at the same time reduces remaining risks and the need for extensive testing. However, current safety approaches do not consider liabilities of traffic participants if a collision occurs. Utilizing formalized traffic rules to verify motion plans allows this problem to be solved. We present a novel approach for verifying the safety of lane change maneuvers, using formalized traffic rules according to the Vienna Convention on Road Traffic. This allows us to provide additional guarantees that if a collision occurs, the self-driving vehicle is not responsible. Furthermore, we consider misbehavior of other traffic participants during lane changes and propose feasible solutions to avoid or mitigate a potential collision. The approach has been evaluated using real traffic data provided by the NGSIM project as well as simulated lane changes.

This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collisionavoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.

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