Su and Fu Receive Best Paper Award at ECCV DriveX Workshop
ECE Assistant Professor Lili Su and Professor Raymond Fu, alongside their research group, received the DriveX@ECCV 2026 Best Paper Award at the DriveX Workshop during the European Conference on Computer Vision (ECCV) 2026 in Malmö for their paper titled “Post-Training in End-to-End Autonomous Driving: Taxonomy, Methods, and Challenges.”
Abstract:
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners. Unlike classic machine learning applications, autonomous vehicles operate in safety-critical and interaction-intensive environments where traditional open-loop imitation of expert demonstrations is not sufficient to ensure reliability. In particular, small execution errors can accumulate over time, while recovery behaviors are scarce in training data. In addition, long-horizon objectives such as safety and driving comfort are not captured by pointwise labels either. These limitations have motivated a shift toward post-training techniques, which further refine driving policies beyond pure imitation. This survey presents a unified view of post-training for autonomous driving by defining its scope and organizing the existing literature into four major families based on the form of supervision they use. For each family, we discuss its capabilities, limitations, and open challenges. We aim to facilitate a systematic understanding of this emerging area and stimulate future research on reliable and efficient post-training for autonomous driving.