考虑交通安全的车载导航技术研究进展

胥川; 胡家琳; 宫丽婷; 江欣国

doi:10.3963/j.jssn.1674-4861.2025.06.001

考虑交通安全的车载导航技术研究进展

doi: 10.3963/j.jssn.1674-4861.2025.06.001

胥川^{1, 2, 3, ,},
胡家琳^{1, 2, 3},
宫丽婷^{1, 2, 3},
江欣国^{1, 2, 3}

1.
西南交通大学交通运输与物流学院成都 611756
2.
综合交通运输智能化国家地方联合工程实验室成都 610031
3.
综合交通大数据应用技术国家工程实验室成都 610031

基金项目:

国家自然科学基金项目 61703352

四川省自然基金面上项目 24NSFSC2298

四川省科技厅应用基础研究项目 2021YJ0042

详细信息

通讯作者:
胥川（1987—），博士，副教授. 研究方向：交通安全. E-mail: xuchuan@swjtu.edu.cn

中图分类号: V328
计量
- 文章访问数: 15
- HTML全文浏览量: 12
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-10
- 网络出版日期: 2026-03-13

In-vehicle Navigation Technology Considering Traffic Safety: A Systematic Literature Review

XU Chuan^{1, 2, 3
, ,},
HU Jialin^{1, 2, 3},
GONG Liting^{1, 2, 3},
JIANG Xinguo^{1, 2, 3}

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu 610031, China
3.
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu 610031, China

摘要

摘要: 随着社会经济发展，交通安全受到的重视程度越来越高，也要求传统的车载导航技术从时间最短的单一效率目标，转向交通效率与安全的综合优化。但现有研究仍存在数据维度不全、用户需求适配性低、多目标权衡难度大等问题，难以适配未来智能交通体系下的大规模应用场景。采用系统性文献综述方法，选取51篇核心非综述文献，围绕数据来源、交通安全水平度量及预测方法、考虑安全的寻路方法、技术验证这4个关键研究问题展开分析，总结领域研究现状与核心需求，为未来智能交通环境下导航技术的发展提供参考与建议。研究发现：数据来源层面，现有研究多依赖单一数据源，且未充分考虑交通场景中的实时、微观特征。采用的安全水平度量及预测方法则多缺乏对不同类型道路单元的因素差异的考虑，以及对于交通运行特征变化趋势的探究，且受数据统计难度与聚合逻辑复杂性影响，难以实现客观度量。考虑安全的寻路方法方面，多数研究将多目标简化为单目标进行优化，但其权重确定缺乏客观依据且动态适配能力不足；而基于最优前沿的寻路方法虽无需权重调整，却在实际求解中面临预设参数限制、计算效率偏低等问题。技术验证方面，真实路测的可靠性最优，但受成本约束难以大规模开展；基于真实数据的推演与交通仿真可有效降低测试成本，却存在实时动态交互信息缺失、与真实交通状况偏差较大等缺陷；主观感受验证虽能补充用户反馈维度信息，仍需权衡样本量、受试者类型带来的成本与有效性问题。针对后续研究，建议聚焦以下4个方向：①建立未来交通信息环境下导航关键数据体系；②在安全水平预测中融入实时交通态势推演；③结合驾驶人异质化驾驶风格与个性化需求，优化寻路技术；④融合大语言模型等新技术，以为导航技术提供智能交互能力支撑。
- 交通安全 /
- 车载导航 /
- 寻路算法 /
- 风险指标 /
- 多目标路径推荐
Abstract: With socioeconomic development, traffic safety receives increasing attention. This trend has driven traditional in-vehicle navigation technologies to shift from a single efficiency-oriented objective (minimizing travel time) toward a comprehensive optimization of traffic efficiency and safety. However, existing studies still suffer from limitations such as incomplete consideration of data dimensions, low adaptability to user needs, and difficulties in multi-objective trade-offs, which hinder their applicability in large-scale scenarios under future intelligent transportation systems. To address these gaps, this study adopts a systematic literature review approach and analyzes 51 core non-review publications. It focuses on four key research questions: data sources, traffic safety level measurement and prediction methods, safety-aware routing approaches, and technical validation strategies. The review synthesizes the current state of research and identifies core demands in the field, providing references and recommendations for the future development of navigation technologies in intelligent transportation environments. The findings indicate that, in terms of data sources, most existing studies rely on a single data source and fail to adequately capture real-time and microscopic traffic characteristics. Regarding safety level measurement and prediction, many approaches lack differentiation among various types of road units and do not sufficiently explore the evolutionary trends of traffic operational characteristics; moreover, due to challenges in data collection and the complexity of aggregation logic, achieving objective safety quantification remains difficult. For safety-aware routing methods, most studies simplify multi-objective problems into single-objective optimization, where the determination of objective weights lacks objective foundations and sufficient dynamic adaptability; although Pareto-front-based methods avoid explicit weight assignment, they face practical challenges such as reliance on preset parameters and relatively low computational efficiency. In terms of technical validation, real-world field tests provide the highest reliability but are constrained by high costs and limited scalability; simulations and data-driven scenario replay based on real data can effectively reduce testing costs, yet they often lack real-time interactive dynamics and may deviate from actual traffic conditions; subjective evaluation can supplement user perception and feedback, but trade-offs among sample size, participant characteristics, cost, and validity must be carefully balanced. Based on these insights, future research is recommended to focus on four major directions: ① establishing key navigation-related data framework under future traffic information environments. ②Integrating real-time traffic state inference into safety level prediction. ③Incorporating heterogeneous driving styles and personalized user needs to enhance routing techniques. ④Leveraging emerging technologies such as large language models to provide intelligent interaction and decision-support capabilities for next-generation navigation systems.
- traffic safety /
- in-vehicle navigation /
- routing algorithms /
- risk indicators /
- multi-objective path recommendation

HTML全文

图 1 年度发表文献数量

Figure 1. Number of annual publications

下载: 全尺寸图片幻灯片

表 1 纳入和排除规则

Table 1. Inclusion and exclusion rules

纳入规则

排除规则

  1.研究主题必须是将交通安全考虑到车载导航技术中
  2.研究以车辆的车载导航为主体，包括描述车辆行驶安全状态的安全指数与车载导航技术
  3.必须提出1种算法或量化方法，而不是评论性的文献

  1.排除不研究或只研究交通安全指数或车载导航优化系统的文献
  2.排除船舶、航空等非道路的考虑安全的车载导航优化系统
  3.排除以犯罪、传染病安全、危险品运输、现金运输等特殊角度考虑的交通安全指数
  4.排除只研究自行车、行人相关的考虑安全的车载导航优化系统
  5.排除综述性、评论文章、商业广告、意见等

下载: 导出CSV

表 2 所涉及研究中路网要素信息数据使用概况

Table 2. Road network data used in relevant study

要素类型	要素信息
路段	路段长度、行程时间；路段限速；几何线形、路段曲率、坡度；路面摩擦、路面质量；车道数及宽度；道路等级；交通信号标志
交叉口	转弯处可见性；进路数量；控制方法

下载: 导出CSV

表 3 所涉及研究中实时交通运行数据使用概况

Table 3. Real-time traffic operation data used in relevant study

交通流类型	交通运行信息
宏观	实时行程时间；平均速度；路线速度、加速度波动；交通流量；交通流密度；拥堵程度
微观	车辆平均速度及波动；车辆加速度及波动；车辆行驶方向、变道；碰撞时间（time to collision，TTC）、后侵入时间（post encroachment time，PET）

下载: 导出CSV

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