most citedEdge Case Detection in Automated Driving: Methods, Challenges, and Future Directions

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cs.RO20261 cited

Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions

Saeed Rahmani, Sabine Rieder, Erwin de Gelder +6

Automated vehicles (AVs) promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains challenging, particularly due…

cs.RO2026

Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning

Saeed Rahmani, Gözde Körpe, Zhenlin +4

Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency. Model Predictive Control (MP…

cs.RO2026

Learning to Drive in New Cities Without Human Demonstrations

Zilin Wang, Saeed Rahmani, Daphne Cornelisse +4

While autonomous vehicles have achieved reliable performance within specific operating regions, their deployment to new cities remains costly and slow. A key bottleneck is the need…

cs.RO2025

Automated Vehicles at Unsignalized Intersections: Safety and Efficiency Implications of Mixed Human and Automated Traffic

Saeed Rahmani, Zhenlin Xu, Simeon C. Calvert +1

The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the inte…

cs.RO2025

A Framework for Human-Reason-Aligned Trajectory Evaluation in Automated Vehicles

Lucas Elbert Suryana, Saeed Rahmani, Simeon Craig Calvert +2

One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension.…