Qingchao Liu , Ruohan Yu , Yingfeng Cai , Quan Yuan , Henglai Wei , Chen Lv
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引用次数: 0
Abstract
There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve driving safety. This study seeks to develop an interpretable system framework for collision risk prediction and takeover requirements analysis (CPTR-LLM) utilizing a large language model (LLM). The model’s inference performance is enhanced through the collection of extensive perception data and the design of a two-stage training strategy, reasoning chain framework, and an error detection and correction mechanism. In terms of collision risk prediction, the experimental results show that the accuracy of CPTR-LLM can reach 0.88. The Cross-sectional-autoregressive-distributed lag (ARDL) model and Augmented Mean Groups (AMG) confirm the reliability of the model’s predictive performance by revealing the association between different variables and collision risk. Regarding takeover requirement analysis, CPTR-LLM accurately comprehends the characteristics of the pre-takeover scene and comprehensively assesses the takeover requirement level in conjunction with collision risk, thereby effectively reducing unnecessary takeovers in simple driving scenarios and unsafe takeovers in scenarios with multiple moving targets. Overall, the findings of this paper offer significant insights into the application and takeover requirements of LLM in the domain of road safety.
期刊介绍:
Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.