arXiv:2606.16558v1 Announce Type: new Abstract: Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry. We present ROSA-RL -- uncertainty-aware Roundabout Optimized Speed Advisory with Reinforcement Learning. It enables safe and efficient roundabout entry for automated and human-driven vehicles in mixed traffic through probabilistic conflict forecasting. A Transformer-based mo
The increasing complexity of mixed-traffic environments and the drive towards fully autonomous vehicles necessitate advanced decision-making systems for scenarios like roundabouts.
This research provides a critical step towards safe and efficient autonomous navigation in challenging urban environments, directly impacting the timeline for widespread AV adoption.
The ability of autonomous vehicles to handle complex, uncertain interactions with human drivers in specific scenarios like roundabouts is improved, enhancing safety and efficiency.
- · Autonomous vehicle manufacturers
- · Smart city infrastructure developers
- · Logistics and transportation companies
- · AI/robotics research institutions
Improved safety and efficiency of autonomous vehicles in urban environments, particularly roundabouts.
Accelerated deployment of Level 4/5 autonomous driving systems in constrained urban areas.
Reduced traffic congestion and accidents in cities as autonomous vehicles become more prevalent and proficient.
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Read at arXiv cs.AI