In recent years, reachability analysis has gained considerable popularity in motion planning and safeguarding of automated vehicles (AVs). While existing tools for reachability analysis mainly focus on general-purpose algorithms for formal verification of dynamical systems, a toolbox tailored to AVspecific applications is not yet available. In this study, we present CommonRoad-Reach, which is a toolbox that integrates different methods for computing reachable sets and extracting driving corridors for AVs in dynamic traffic scenarios. Our toolbox provides a Python interface and an efficient C++ implementation for real-time applications. The toolbox is integrated within the CommonRoad benchmark suite and is available at commonroad.in.tum.de.

Motion planners for autonomous vehicles must obtain feasible trajectories in real-time regardless of the complexity of traffic conditions. Planning approaches that discretize the search space may perform sufficiently in general driving situations, however, they inherently struggle in critical situations with small solution spaces. To address this problem, we prune the search space of a sampling-based motion planner using reachable sets, i.e., sets of states that the ego vehicle can reach without collision. By only creating samples within the collisionfree reachable sets, we can drastically reduce the number of required samples and thus the computation time of the planner to find a feasible trajectory, especially in critical situations. The benefits of our novel concept are demonstrated using scenarios from the CommonRoad benchmark suite.

The validation of online perception algorithms in automotive systems requires a large amount of ground-truth data. Since manual labeling is inefficient and error-prone, an automatic generation of accurate and reliable reference data is desirable. We present a post-processing approach based on a particlebased dynamic occupancy grid representation of the environment. In contrast to existing online dynamic grid algorithms, our estimation additionally utilizes future measurements by applying offline smoothing algorithms. Our proposed concept uses a twofilter procedure for smoothing the occupancy states of the grid cells. We further introduce two methods based on particle reweighting and two-filter smoothing to improve the velocity estimates. We show that our approach enhances the situational awareness and thus provides a more precise environment model. We demonstrate these benefits using lidar data from real-world experiments.