Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment
Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment
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DOI：
10.1016/j.trc.2017.01.003
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发表时间：
2017-03-01
影响因子：
8.3
通讯作者：
Yang, Y. Jeffrey
中科院分区：
文献类型：
--
作者：
Liu, Hao;Wei, Heng;Yang, Y. Jeffrey
在车辆与路边基础设施可以无线通信的车联网环境下，高级驾驶辅助系统（ADAS）可以作为执行器，实现高速公路设施的交通安全和移动性优化。在这方面，交通管理中心需要确定能够优化交通安全和移动性能的最佳 ADAS 算法参数集，并将最佳参数集以无线方式广播给各个配备 ADAS 的车辆。一旦配备 ADAS 的驾驶员执行了最佳参数集，他们就会成为积极的代理，协同工作以防止交通冲突，并抑制交通振荡发展为严重交通拥堵。衡量这种交通管理的系统有效性需要分析能力来捕获 ADAS 对单个驾驶员行为的量化影响以及由于这种影响而带来的总体交通安全和流动性改善。为此，本研究提出了一种综合方法，将受 ADAS 影响的驾驶行为建模和最先进的微观交通流建模融入虚拟模拟环境中。在此环境的基础上，通过使用遗传算法的多目标优化方法来确定最佳 ADAS 算法参数集。所开发的方法在低、中、高 ADAS 市场渗透率场景下的高速公路设施上进行了测试。案例研究表明，在中高渗透率场景下，微调ADAS算法参数可以显着提高吞吐量并减少研究地点的流量延迟和冲突。在这些场景下，ADAS算法参数优化是必要的。否则，ADAS 会加剧驾驶员之间的行为异质性，导致交通安全改善甚微，并对出行产生负面影响。在高渗透率场景下，确定的最佳ADAS算法参数集可用于支持不同的控制目标（例如，安全性改进优先与移动性改进优先）。 (C) 2017 Elsevier Ltd. 保留所有权利。
Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers' behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and stateof-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority). (C) 2017 Elsevier Ltd. All rights reserved.