Resilience represents a crucial safety paradigm characterizing system risk evolution under specific perturbations, yet its application to microscopic driving safety needs further exploration. Analogous to resilience evolution patterns, generalized driving risk progression exhibits distinct safety decay and recovery phases, thereby establishing theoretical foundations for driving safety resilience analysis. This study develops a resilience-centric framework for quantifying dynamic car-following risk evolution. The car-following pairs were selected from highD natural driving trajectory dataset. Following risk quantification and clustering, 1,676 samples demonstrating complete “safe-dangerous-safe” transition process were identified. Through decomposition of car-following resilience into safety decay and recovery phases, we derived five interpretable resilience features and modeled their functional dependencies on driving variables via an integrated machine learning and SHapley Additive exPlanations (SHAP) analytical framework. Key findings reveal: 1) the CatBoost demonstrated superior fitting performance, achieving a mean absolute percentage error less than 10% across all resilience features; 2) the proposed Low-Rank Polynomial SHAP Fitting (LRP-SF) captured the nonlinear relationships between driving variables and resilience features, quantifying both directional influences and threshold effects; and 3) driver risk perception exhibits phase-dependent variability, with a stimulus-response mechanism governing safety evolution dynamics. The rapid safety deterioration and hazardous states act as triggers for recovery processes. This study further examined resilience threshold validity, car-following variable interactions, and inter-feature correlations. The potential application of LRP-SF in car-following safety control was also anticipated. This study offers methodological advancements for advanced driver assistance systems development and establishes a novel analytical paradigm for driving safety research.

Predicting and controlling car-following risks is a crucial component of advanced driver-assistance systems (ADAS). The traditional control framework activates only on reaching a specific risk threshold, which may not be timely enough to prevent collisions. This research proposes an active safety control framework based on prediction of safety evolution patterns. First, car-following samples are extracted from natural driving data, and safety and risk boundaries determined. The car-following patterns are conceptually divided into Safety, Disturbance, and Resilience based on the safety evolution trend. Subsequently, a proposed convolutional neural network (CNN) with gated recurrent unit (GRU) and with feature and temporal attention mechanisms is employed to predict the safety evolution pattern. Finally, a weight-optimized model predictive control (MPC) is applied for the safety control of samples predicted as Resilience. The results indicate that: (1) the proposed risk pattern prediction model achieves an average F1 score of 0.931, outperforming other baseline models; (2) the optimal weight combination for MPC is determined through grid search, resulting in an overall driving safety improvement of 29.31%, with the safety of all controlled samples remaining above the risk boundary and the proposed framework achieving significant improvements over traditional threshold-based solutions; and (3) there are distribution differences in the variables across safety evolution patterns, and the falsely predicted samples also exhibit feature distribution similarities to other patterns. The trade-off exists between safety and efficiency in the control algorithm. This study validates the effectiveness of the risk pattern prediction and safety control framework, contributing to the advancement of ADAS technology.

Driving risk prediction is crucial for advanced driving technologies, with deep learning approaches leading the way in driving safety analysis. Current driving risk prediction methods typically establish a mapping between driving features and risk statuses. However, status prediction fails to provide detailed risk sequence information, and existing driving safety analyses seldom focus on run-off-road (ROR) risk. This study extracted 660 near-roadside lane-changing samples from the high-D natural driving dataset. The performance of sequence and status prediction for ROR risk was compared across five mainstream deep learning models: LSTM, CNN, LSTM-CNN, CNN-LSTM-MA, and Transformer. The results indicate the following: (1) The deep learning approach effectively predicts ROR risk. The Macro F1 Score of sequence prediction significantly surpasses that of status prediction, with no notable difference in efficiency; (2) Sequence prediction captures risk evolution trends, such as increases, turns, and declines, providing more comprehensive safety information; (3) The presence of surrounding vehicles significantly impacts lane change duration and ROR risk. This study offers new insights into the quantitative research of ROR risk, demonstrating that risk sequence prediction is superior to status prediction in multiple aspects and can provide theoretical support for the development of roadside safety.

As autonomous-driving technology advances, autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to coexist for an extended period. While resilience is widely applied in macro safety analysis, its application in micro-process modeling remains underdeveloped. This study proposes a resilience-based framework comprising three phases (initial, safety decay, safety recovery) to investigate the evolution of driving risk across three car-following scenarios: HV–HV (HV following HV), HV–AV (HV following AV), and AV–HV (AV following HV). The analytical framework consists of three steps: First, car-following pairs with complete risk-evolution processes were identified. Second, cluster analysis was applied to categorize phase-specific patterns. Third, the association rule mining algorithm was used to trace risk-evolution chains, followed by a comprehensive evaluation integrating safety and efficiency. Key findings include: (1) significant and rapid safety decay being accompanied by swift recovery, and the following vehicle exhibiting significant feature differences across recovery patterns; (2) safety decay in HV–AV being slower than that in HV–HV, with HV–AV demonstrating a close and conservative driving strategy during the risk-evolution process, with HV–AV meanwhile performing worse in safety and efficiency compared with HV–HV, highlighting the interaction discordance between HV and AV; (3) excessive deceleration of HV being the primary trigger for safety degradation in AV–HV, and AV demonstrating effective adaptation to safety decay, promoting slight safety reduction with rapid recovery, thereby balancing safety and efficiency. These findings reveal principles of risk evolution in mixed-driving environments, providing a novel analytical framework for mixed-driving safety.