Negotiating Cooperative Ordering Problems with Bimodal Planning
Raphael Wenzel, Malte Probst, Tim Puphal, Markus Amann, Julian P. Eggert · 2025
In Automated Driving (AD), traffic scenarios where two agents must resolve an ordering without knowing each other's intention are critical for expanding the operational design domain of automated vehicles to urban environments. These scenarios require negotiation to determine who passes first through an interaction zone. We present a novel agreement measure and negotiation approach to resolve these ordering problems across a wide range of common scenarios. Our method emphasizes detecting and deciding when to switch between potential negotiation outcomes. Our approach extends existing behavior planners to cope with bimodal cooperative interactions, where two potentially desirable outcomes need to be considered. We evaluate our approach by providing both an illustrative scenario and extensive statistical experiments across various geometries, including oncoming narrow passages, crossing and merging scenarios. The results demonstrate that our system considerably improves the behavior in cooperative ordering scenarios compared to the baseline. Furthermore, it is also robust in the sense that it effectively handles dynamic situations where the other agent's intentions changes during the negotiation process.