Tiantian Chen

The widely adopted SAE (Society of Automotive Engineers) taxonomy categorizes driving automation primarily based on the division of operational responsibilities between the human driver and the system. This has worked well for several applications. However, it fails to distinguish the requisite versatility of autonomous vehicle (AV) performance across diverse operational environments. This structural limitation has spawned critical challenges, including safety risks, commercialization barriers, regulation challenges, and inadequate Operational Design Domain (ODD) development guidelines toward Level 5 autonomy. To address these gaps, this paper proposes a supplemental taxonomy for SAE Levels 3 and 4 using an Environmental-Situational Capability (ESC) evaluation framework across three core dimensions: the built environment, natural environment, and ambient traffic conditions. Using a two-stage hierarchical classification strategy, the ESC framework maps expert evaluations of a specific automated driving system (ADS) to corresponding levels of the proposed taxonomy. This novel classification bridges the ADS level and the ODD by correlating environmental complexity with the minimum capabilities required for safe autonomous operation. Operating orthogonally to SAE standards, this taxonomy categorizes systems based on their actual operational capabilities rather than driving automation task responsibility alone. Consequently, it establishes a crucial alignment across researchers, manufacturers, regulators, and consumers, providing a practical reference for product benchmarking, safety validation, and policymaking.

This project develops an offline reinforcement learning framework for optimizing speed guidance of connected autonomous vehicles (CAVs) at signalized intersections under mixed traffic conditions. The framework learns safe and efficient policies from real-world trajectory data without requiring risky online interactions. The system employs Critic Regularized Regression (CRR) combined with real-world driving trajectories and Signal Phase and Timing (SPaT) information from the UCF-SST-CitySim dataset. This approach bridges the sim-to-real gap by learning directly from naturalistic human driving behavior while incorporating safety-critical features often neglected in prior work.

A project for a study on LLM-designed cooperative control strategies for connected and automated vehicles at unsignalized intersections. It includes the Python source code, SUMO simulation files, processed scenario data, experiment logs, and documentation needed to reproduce and inspect the main computational experiments.