Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment

--This paper presents a unified framework for the evaluation and optimization of autonomous vehicle trajectories, integrating formal safety, comfort, and efficiency criteria. An innovative geometric indicator, based on the analysis of safety zones using adaptive ellipses, is used to accurately quantify collision risks. Our method applies the Shoelace formula to compute the intersection area in the case of misaligned and time-varying configurations. Comfort is modeled using indicators centered on longitudinal and lateral jerk, while efficiency is assessed by overall travel time. These criteria are aggregated into a comprehensive objective function solved using a PSO-based algorithm. The approach was successfully validated under real traffic conditions via experiments conducted in an urban intersection involving an autonomous vehicle interacting with a human-operated vehicle, and in simulation using data recorded from human driving in real traffic. Current research on autonomous vehicles and intelligent transport systems underlines the necessity for advanced decision-making frameworks that effectively manage multiple objectives. Among these objectives, safety retains the highest priority, requiring the vehicles to not only avoid collisions, but also to comply with traffic rules as well as exhibit a predictable behavior in complex urban environments. While safety is paramount, it is also essential to maintain the system's efficiency by optimizing traffic flows, minimizing delays, and reducing congestion, especially as transport infrastructures become increasingly interconnected. In light of the above, it is clear that balancing safety, efficiency, and comfort is not just a conceptual ideal but rather a requirement that shapes autonomous vehicle decision-making frameworks.