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.@en

Falsification aims to disprove the safety of systems by providing counter-examples 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 rapidly-exploring 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.@en