An enhanced automatic emergency braking system integrated with high-precision maps

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.