Learning-Based Risk-Perception Strategies for Intelligent Co-Control of Energy Efficiency and Safety in Autonomous Driving: A Survey

Abstract The integration of intelligent transportation systems and autonomous driving is reshaping modern mobility by mitigating the longstanding trade-off between traffic efficiency and road safety. Enabled by vehicle-to-everything (V2X) communications, connected and autonomous vehicles (CAVs) are increasingly integrated into vehicle–road–cloud collaborative networks, resulting in measurable improvements in traffic capacity, energy efficiency, and collision avoidance. However, the coexistence of CAVs, human-driven vehicles, and vulnerable road users (VRUs) introduces complex challenges in collaborative control, real-time risk perception, and data security. To address these issues, this review synthesizes recent advances through a tripartite lens: collaborative driving, risk-aware perception, and energy-efficient operation. Our analysis identifies three recurring scientific questions: (i) how to effectively couple dynamic risk perception with cooperative control under uncertainty; (ii) how to enable secure, privacy-preserving collaboration across heterogeneous agents; and (iii) how to guarantee VRU safety in mixed-autonomy traffic. Representative approaches fall into three categories: (1) multimodal collaborative decision-making frameworks combining hierarchical deep reinforcement learning and model predictive control; (2) federated learning architectures that preserve data privacy while enabling cross-vehicle knowledge sharing; and (3) human-centric safety mechanisms leveraging ultra-wideband sensing and heterogeneous graph neural networks for VRU detection and intent prediction. Collectively, these findings demonstrate that risk-aware, energy-efficient cooperative driving is technically feasible, yet its large-scale deployment hinges on interdisciplinary innovation, standardized communication protocols, and regulatory alignment.