Hidden traffic participants pose a great challenge for autonomous vehicles. Previous methods typically do not use previous obser-vations, leading to over-conservative behavior. In this paper, we present a continuation of our work on reasoning about objects out-side the current sensor view. We aim to demonstrate our recently proposed method on an autonomous platform and evaluate its relia-bility and real-time feasibility when using real sensor data. Showing a significant driving performance increase on a real platform, with-out compromising safety, would be a significant contribution to the field of autonomous driving.

Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.

Ensuring the safety of autonomous vehicles (AV s) in uncertain traffic scenarios is a major challenge. In this paper, we address the problem of computing the risk that AV s violate a given safety specification in uncertain traffic scenarios, where state estimates are not perfect. We propose a risk measure that captures the probability of violating the specification and determines the average expected severity of violation. Using highway scenarios of the US101 dataset and Responsible Sensitive Safety (RSS) as an example specification, we demonstrate the effectiveness and benefits of our proposed risk measure. By incorporating the risk measure into a trajectory planner, we enable AVs to plan minimal-risk trajectories and to quantify trade-offs between risk and progress in traffic scenarios.