交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 125-134.DOI: 10.16097/j.cnki.1009-6744.2026.01.012

• 智能交通系统与信息技术 • 上一篇    下一篇

低能见度下智能汽车双因子避障轨迹优化研究

尚婷*1a,1b，胥浩1c，毛慧涵1c，何军2   

  1. 1. 重庆交通大学，a.交通运输学院，b.智能综合立体交通重庆市重点实验室，c.土木工程学院，重庆400074；2. 重庆市气象服务中心，重庆401147
  • 收稿日期:2025-10-24 修回日期:2025-12-02 接受日期:2026-01-06 出版日期:2026-02-25 发布日期:2026-02-15
  • 基金资助:
    教育部青年人文社会科学研究青年基金(22YJCZH143)；重庆市研究生科研创新项目(CYS25494)。

Dual-factor Optimization of Collision Avoidance Trajectories for Intelligent Vehicles Under Low-visibility Conditions

SHANG Ting*1a,1b, XU Hao1c, MAO Huihan1c, HE Jun2   

  1. 1a. School of Traffic & Transportation, 1b. Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, 1c. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. Chongqing Meteorological Bureau, Chongqing 401147, China
  • Received:2025-10-24 Revised:2025-12-02 Accepted:2026-01-06 Online:2026-02-25 Published:2026-02-15
  • Supported by:
    Youth Foundation for Humanities and Social Science Research of Ministry of Education (22YJCZH143)；Chongqing Postgraduate Research Innovation Project (CYS25494)。

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摘要： 为提升智能汽车在低能见度条件下的紧急避障性能，本文基于无人机现场实测交通流数据，构建低能见度环境下的轨迹规划与自适应优化模型。首先，建立融合连续评分与监督学习的转向决策机制。结果表明，在低能见度条件下，模型方向判定一致率达93%，较传统阈值法提升39.6%，平均响应时间为0.12s，避撞条件触发的准确率达96%。其次，分别采用五次多项式法与分段式四阶贝塞尔法进行1000次轨迹生成仿真。对比结果显示，前者平均运行时间为0.0461s，后者为0.0385s，计算效率提高16.45%；贝塞尔法的最大曲率变化率为0.003m-2，显著低于五次多项式法的0.094m-2，表明其轨迹平滑性与实时性更优。进一步，提出能见度与附着系数双因子微调机制实现动态修正。当能见度为440~490m、附着系数为0.54~0.60时，中段控制点修正最为显著，横向偏移增幅约3.8%；当hvis≥470m、μ≥0.57时，曲率变化率稳定在0.027 m-1·s-1以内，系统响应时间小于0.12s。研究结果验证了双因子自适应优化模型在低能见度与低附着工况下的有效性与鲁棒性，可为复杂气象条件下智能汽车的轨迹控制与安全避障提供可工程化的实现路径。

关键词: 智能交通, 紧急避障, 贝塞尔曲线, 智能汽车, 低能见度, 自适应调节

Abstract: To enhance the emergency collision avoidance performance of intelligent vehicles under low-visibility conditions, this study proposes a trajectory planning and adaptive optimization model based on traffic flow data collected through unmanned aerial vehicle (UAV). First, a steering decision mechanism combining continuous scoring and supervised learning is developed. The results indicate that under low-visibility conditions, the model achieves a directional decision consistency of 93%, representing a 39.6% improvement over the traditional threshold-based method. The average response time is 0.12 seconds, and the accuracy of collision-avoidance trigger detection reaches 96%. Furthermore, 1 000 trajectory generation simulations were conducted using the quintic polynomial and segmented fourth-order Bézier methods. The comparative results indicate that the average computation times of the two methods are 0.046 1 seconds and 0.038 5 seconds, respectively, demonstrating a 16.45% increase in computational efficiency. The maximum curvature variation of the Bézier method is 0.003 m-², which is significantly lower than that of the quintic polynomial method (0.094 m-²), confirming its superior trajectory smoothness and real-time performance. In addition, a dual-factor fine-tuning mechanism based on visibility and adhesion coefficient is proposed to achieve dynamic correction. When the visibility ranges from 440 to 490 meters and the adhesion coefficient from 0.54 to 0.60, the mid-section control points show the most significant adjustments, with a 3.8% increase in lateral offset. When hvis ≥470 m and μ≥0.57, the curvature variation stabilizes within 0.027m-¹⋅s-¹,and the system response time remains below 0.12 seconds. The results showed the effectiveness and robustness of the dual-factor adaptive optimization model under low-visibility and low-adhesion conditions, providing an engineering oriented framework for trajectory control and safe collision avoidance of intelligent vehicles in complex weather conditions.

Key words: intelligent transportation, emergency obstacle avoidance, Bézier curve, intelligent vehicle, low visibility, adaptive adjustment

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