Jin Weimin, Islam Mhafuzul, Chowdhury Mashrur
Arcadis U.S., Inc, 10205 Westheimer Road, Suite 800, Houston, TX 77042, USA.
General Motors R&D, Warren, MI 48092, USA.
J Safety Res. 2022 Dec;83:45-56. doi: 10.1016/j.jsr.2022.08.003. Epub 2022 Aug 13.
The safe freeway merging operation for fully Autonomous Vehicles (AVs) in mixed traffic (i.e., the presence of AVs and non-AVs in a traffic stream) is a challenging task. Under a mixed traffic environment, an AV merging operation could significantly increase conflict risks and reduce operational efficiency.
This study quantifies the freeway merging conflict risk and develops a freeway merging decision strategy based on conflict risk assessment for an AV attempting to merge to a traffic stream with non-AVs on the freeway. The performance of the risk-based merging decision strategy is evaluated in uncongested, near-congested, and congested traffic conditions.
The analyses show that the risk-based merging strategy causes less abrupt deceleration of an AV's immediate upstream vehicle in the target lane on the freeway compared to the based models (i.e., two models based on gap acceptance concepts and a safe gap model based on a surrogate measure, 'Time-to-Collision (TTC)'). The risk-based merging strategy meets the minimum safe gap between an AV intending to merge and its immediate downstream vehicle in the target lane. The risk-based merging strategy produces lower conflict risk in terms of 'Time Exposed Time-to-Collision (TET)' and 'Time Integrated Time-to-Collision (TIT)' compared to the base models. Moreover, the risk-based merging strategy has a lower impact on the average speed of traffic in the target lane compared to the base models considered in this study.
The risk-based merging strategy shows higher safety benefits for an AV's merging operation compared to base models.
The findings of this research would help design AV controllers for improving the safety of an AV merging operation in a mixed traffic stream.
在混合交通(即交通流中存在自动驾驶车辆和非自动驾驶车辆)中,全自动驾驶车辆(AV)在高速公路上的安全合并操作是一项具有挑战性的任务。在混合交通环境下,自动驾驶车辆的合并操作可能会显著增加冲突风险并降低运营效率。
本研究对高速公路合并冲突风险进行了量化,并基于冲突风险评估为试图在高速公路上与非自动驾驶车辆合并到交通流中的自动驾驶车辆制定了高速公路合并决策策略。在不拥堵、接近拥堵和拥堵的交通状况下评估了基于风险的合并决策策略的性能。
分析表明,与基于间隙接受概念的两种模型和基于替代指标“碰撞时间(TTC)”的安全间隙模型等基础模型相比,基于风险的合并策略导致高速公路目标车道中自动驾驶车辆紧邻的上游车辆的急刹车情况更少。基于风险的合并策略满足了试图合并的自动驾驶车辆与其在目标车道中紧邻的下游车辆之间的最小安全间隙。与基础模型相比,基于风险的合并策略在“暴露时间到碰撞(TET)”和“时间积分到碰撞(TIT)”方面产生的冲突风险更低。此外,与本研究中考虑的基础模型相比,基于风险的合并策略对目标车道交通平均速度的影响更小。
与基础模型相比,基于风险的合并策略在自动驾驶车辆的合并操作中显示出更高的安全效益。
本研究结果将有助于设计自动驾驶车辆控制器,以提高混合交通流中自动驾驶车辆合并操作的安全性。