Falsification aims to disprove the safety of systems by providing counterexamples that lead to a violation of safety properties. In this work, we present two novel falsification methods to reveal safety flaws in adaptive cruise control (ACC) systems of automated vehicles. Our methods use rapidlyexploring random trees to generate motions for a leading vehicle such that the ACC under test causes a rear-end collision. By considering unsafe states and searching backward in time, we are able to drastically improve computation times and falsify even sophisticated ACC systems. The obtained collision scenarios reveal safety flaws of the ACC under test and can be directly used to improve the system's design. We demonstrate the benefits of our methods by successfully falsifying the safety of state-of-the-art ACC systems and comparing the results to that of existing approaches.
To allow autonomous vehicles to safely participate in traffic and to avoid liability claims for car manufacturers, autonomous vehicles must obey traffic rules. However, current traffic rules are not formulated in a precise and mathematical way, so that they cannot be directly applied to autonomous vehicles. Additionally, several legal sources other than national traffic laws must be considered to infer detailed traffic rules. Thus, we formalize traffic rules for interstates based on the German Road Traffic Regulation, the Vienna Convention on Road Traffic, and legal decisions from courts. This makes it possible to automatically and unambiguously check whether traffic rules are being met by autonomous vehicles. Temporal logic is used to express the obtained rules mathematically. Our formalized traffic rules are evaluated for recorded data on more than 2,500 vehicles.
Adaptive cruise control is one of the most common comfort features of road vehicles. Despite its large market penetration, current systems are not safe in all driving conditions and require supervision by human drivers. While several previous works have proposed solutions for safe adaptive cruise control, none of these works considers comfort, especially in the event of cut-ins. We provide a novel solution that simultaneously meets our specifications and provides comfort in all driving conditions, including cut-ins. This is achieved by an exchangeable nominal controller ensuring comfort, combined with a provably correct fail-safe controller that gradually engages an emergency maneuver-this ensures comfort, since most threats are already cleared before emergency braking is fully activated. As a consequence, one can easily exchange the nominal controller without having to have the overall system safety re-certified. We also provide the first user study into a provably-correct adaptive cruise controller. It shows that even though our approach never causes an accident, passengers rate the performance as good as a state-of-the-art solution that does not ensure safety.
Ensuring the safety of autonomous vehicles is a challenging task, especially if the planned trajectories do not consider all traffic rules or they are physically infeasible. Since replanning the complete trajectory is often computationally expensive, efficient methods are necessary for resolving such situations. One solution is to deform or repair an initiallyplanned trajectory, which we call trajectory repairing. Our approach first detects the part of an invalid trajectory that can stay unchanged. Afterward, we use a hierarchical structure and our novel sampling-based algorithm informed closedloop rapidly-exploring random trees (informed CL-RRTs) to efficiently repair the remaining part of the trajectory. We evaluate our approach with different traffic scenarios from the CommonRoad benchmark suite. The computational efficiency is demonstrated by comparing the computation times with those required when replanning the complete trajectory.
Maps are essential for testing autonomous driving functions. Several map and scenario formats are available. However, they are usually not compatible with each other, limiting their usability. In this paper, we address this problem using our open-source toolbox that provides map converters from different formats to the well-known CommonRoad format. Our toolbox provides converters for OpenStreetMap, Lanelet/Lanelet2, OpenDRIVE, and SUMO. Additionally, a graphical user interface is included, which allows one to efficiently create and manipulate CommonRoad maps and scenarios. We demonstrate the functionality of the toolbox by creating CommonRoad maps and scenarios based on other map formats and manually-created map data.
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