ALERT: A Self-evolving Agent for Latent Risk Reasoning in Autonomous Driving

Safety-critical events in autonomous driving often develop progressively before explicit conflicts emerge. However, existing approaches mainly focus on detecting manifested hazards or predicting short-term outcomes, leaving the early-stage risk evolution insufficiently explored. In this work, we introduce ALERT, a self-evolving agent framework for latent risk reasoning that aims to identify how observable cues may evolve into future driving risks. ALERT employs a mechanism-oriented risk routing taxonomy to organize diverse risk formation processes, uses tool-augmented reasoning to acquire driving-specific evidence, and continuously improves its Skill Pool through controlled evolution. To enable systematic evaluation, we construct a latent risk reasoning benchmark from real-world takeover data covering risk stages, risk types, risk subjects and causal reasoning. ALERT outperforms the strongest VLM-only baseline by 11.7% in overall score and further surpasses knowledge-access baselines, particularly in risk-subject identification and reasoning. Controlled Skill evolution yields an additional 4.9% improvement over the warm-up stage, demonstrating the value of accumulating reusable reasoning experience for latent risk understanding.