The effectiveness of traditional automatic emergency braking (AEB) systems in mitigating traffic accident severity has been validated; however, their performance remains significantly inadequate under rainy conditions, at high speeds, at intersections, and in complex traffic scenarios. To address these limitations, this paper proposes an enhanced AEB system designed for multiscenario operation. By incorporating a high-precision map (prior information) and a systematic risk modeling mechanism, the system aims to improve the recognition of collisions and the rationality of braking activation, thereby enhancing adaptability in specific traffic environments.

First, system-theoretic process analysis was performed to analyze the information flow across the perception, decision, control, execution, and environment layers of the AEB system. This analysis identified safety-critical control behaviors within the control loop and combined them with typical failure modes to construct a structured set of unsafe control behaviors, providing traceable targets for root cause analysis and strategy refinement. Building on this framework, the root causes of unsafe control behaviors were categorized into two coupled mechanisms: input bias on the perception side and temporal-logical defects on the decision side. The former included positioning and environmental information errors arising from sensor hardware limitations, environmental interference, and information fusion defects. The latter involved decision instability caused by inadequate risk assessment and inappropriate strategies. To quantify the relative influence of multidimensional risk factors and guide parameter optimization, an analytic hierarchy process-based risk weighting model was developed. This model assigned weights to factors such as vehicle motion state, road geometric constraints, and environmental interference, thereby forming a quantitative risk weighting system that linked scenario characteristics to triggering behaviors. Building on this foundation, an enhanced collision time metric, T1, that integrated high-precision maps was developed. Using AHP-weighted scenario coefficients, T1 is dynamically adjusted, enabling a more rational determination of AEB triggering timing based on roadway geometry, traffic semantics, and environmental conditions. Finally, real-vehicle tests were conducted at the Dongfeng Intelligent Connected Vehicle Demonstration Zone using a BYD Han EV platform for validation.

Real-world test results demonstrated that the proposed AEB system significantly outperforms traditional AEB systems in representative scenarios. Relative to conventional AEB strategies, the proposed system achieved a 27.9% reduction in average collision speed at high speeds and a 75.0% increase in the collision avoidance rate. Under rainy conditions, the collision speed decreased by 48.7%, and the avoidance rate improved by 79.9%. In pedestrian-related intersection tests, the conventional and proposed systems brought the vehicle to a complete stop before a collision; however, the latter system achieved a stopping distance closer to the ideal safety margin range of 1.0—1.5 m, indicating reduced overconservative intervention and a lower false-trigger rate. In the combined high-speed and rainy scenario, the collision speed was reduced by 31.2%, and the collision avoidance rate increased by 44.4%. The T1 metric integrated with high-precision maps enabled earlier intervention at high speeds and delayed triggering at intersections, enhancing decision consistency and braking activation rationality without compromising deceleration capability.

The proposed model provides an interpretable, practical, and robust approach for improving the adaptability and reliability of AEB systems in complex traffic environments. By leveraging high-precision maps to achieve scenario-adaptive risk perception and trigger optimization, the proposed model effectively addresses the limitations of traditional approaches and offers important methodological support for designing next-generation safety-critical braking systems in intelligent vehicles. Future work will further consider multiparticipant interactions, refined environmental modeling, and variations in vehicle load to extend the model's applicability.

In the context of the swift progression of autonomous driving technology, the widespread reliance of current systems on uniform behavioral models for decision-making and path planning is a crucial concern. This generalized approach often disregards variations in driving behavior among different drivers, making it challenging to achieve driving behavior that aligns with drivers' expectations in complex and dynamic traffic scenarios. Consequently, a decrease in comfort and trust is observed in autonomous vehicles. This study focuses on lane changing, a common yet critical driving maneuver, aiming to optimize planning strategies by incorporating drivers' characteristics to match individual driving styles.

This study comprehensively analyzes data derived from naturalistic driving experiments. Kalman filtering is used to detect and eliminate anomalies in raw data, thereby reducing noise interference. The integration of temporal constraints into the fuzzy C-means clustering algorithm ensures the preservation of chronological order in the clustered data, which is essential for analyzing sequential events such as lane change maneuvers. Lane changing requires lateral and longitudinal vehicle control with distinct operational characteristics across different phases of the maneuver. By clustering the entire lane-changing process data into three major categories, C1, C2, and C3, representing the preparation, execution, and completion stages of lane changing, respectively, this study aims to analyze disparities in driver behavior during these distinct phases. According to the characteristics of lane-changing scenarios, relevant variables are selected for in-depth examination. Independent sample t-tests are then conducted among different drivers for each variable, and variables with a high proportion of insignificant t-values are eliminated. This process helps identify personalized indicators that reflect driver-specific traits during lane changing. Subsequently, an artificial potential field (APF) model is established for the lane-changing scenario. The APF method uses virtual attractive and repulsive forces to guide the vehicle toward a path of decreasing potential energy, effectively avoiding obstacles while moving toward the target position. Variations in the APF parameters lead to different planning paths. By leveraging the extracted personalized indicator, the APF model for lane changing is customized, yielding paths that align with individual driving styles. Another pivotal consideration is the planning of lane-changing speeds. Given the notable variations in the speed preferences of drivers, this study proposes a lane-changing speed planning algorithm based on a quintic polynomial function. This ensures that the mean duration of acceleration and the maximum acceleration limit during the execution phase align with each driver's speed control habits and that a smooth velocity profile is maintained throughout the lane-changing maneuver.

This study proposes a lane-changing planning method for autonomous vehicles that considers driver differences. The simulation results confirm that the proposed personalized lane-changing planning approach not only produces paths that align with individual driving styles but also regulates lane-changing velocities in accordance with each driver's operational habits. By quantifying behavioral variations, developing personalized APF models, and implementing customized speed planning strategies, this study exemplifies how to tackle individualization challenges in autonomous driving. This study represents a step forward in advancing autonomous vehicle technology toward a human-centric and intelligent future.

Most current automatic emergency-braking (AEB) systems perceive the surrounding environment through on-board sensors, which generally encounter the following issues: the cost of lidar is high, their performance in the presence of smoke medium and rain and snow weather is imprecise and is restricted by long-distance energy loss, and the millimeter-wave radar can only sense obstacles in a short distance. The monocular/binocular camera is greatly affected by objective factors, such as reduced visibility due to weather and nighttime, resulting in a small observation distance. At the intersection, the road traffic environment is complex, specifically when a commercial vehicle has a remarkable blind spot, and the function of the vehicle sensor is greatly limited.

To improve the safety and reliability of AEB systems, this work designs and studies an AEB system for commercial vehicles based on unsafe control behavior. First, a compensation method is proposed on the basis of the characteristics of vehicle-to-vehicle communication delay under different conditions. Real vehicle tests are conducted to collect data regarding the communication delay of vehicle-mounted communication equipment transmitting self-vehicle information under different working conditions. The average value is taken as the delay compensation in the safety distance and then added as compensation data to the established safety distance model in the AEB system based on vehicle-road coordination. The delay law is used to correct parameters such as the speed, displacement, and coordinates of the environmental vehicle to compensate for the impact of communication delay on system decision-making. An AEB strategy for commercial vehicles at the intersection section is described. The contours of the two vehicles are projected onto a coordinate system to determine whether the two vehicles overlap. When a collision risk is detected, the collision avoidance strategy of the two vehicles at the road intersection is implemented. When the two vehicles are about to collide, the braking system of the vehicle is controlled to brake automatically and urgently with maximum braking deceleration to avoid collision. Furthermore, the unsafe control behavior causing the accident is determined through analysis, and the corresponding safety constraints are used to optimize the algorithm strategy. Finally, the proposed algorithm is simulated and tested.

Results show that the proposed AEB algorithm based on unsafe control behavior can effectively prevent the collision of two vehicles at the intersection and has high safety and reliability.

This study has a few limitations and shortcomings. This work only considers the influence of communication delay and braking onset stage on the safe braking distance, and the collision avoidance strategy only considers the scene of a straight intersection. In future research, consideration will be given to the factors affecting the ability to obtain an accurate and safe braking distance, and 5G technology will be gradually applied to an AEB system based on vehicle-road coordination.