A vehicle speed harmonization strategy for minimizing inter-vehicle crash risks.

Recent technological advancements have facilitated the implementation of speed harmonization based on connected and automated vehicles (CAV) to prevent crashes on the road. In addition, trajectory-level vehicle controls are receiving substantial attention as sensors, wireless communications, and control systems are rapidly advancing. This study proposes a novel vehicle speed control strategy to minimize inter-vehicle crash risks in automated driving environments. The proposed methodology consists of the following three components: a risk estimation module, a risk map construction module, and a vehicle speed control module. The essence of the proposed strategy is to adjust the subject vehicle speed based on an analysis of the interactions among a subject vehicle and the surrounding vehicles. Crash risks are quantified by a fault tree analysis (FTA) method to integrate the crash occurrence potential and crash severity at every time step. A crash risk map is then constructed by projecting the integrated risk of the subject vehicle into a two-dimensional space composed of relative speed and relative spacing data. Next, the vehicle speed is continuously controlled to reach the target speed using risk map analysis to prevent a crash. The performance of the proposed methodology is evaluated by a VISSIM simulator with various traffic congestion levels and market penetration rates (MPR) of controlled vehicles. For example, an approximate 50% reduction rate of the crash potential was achievable without a loss of the operational performance of the traffic stream when all vehicles were controlled by the proposed methodology under the level of service (LOS) C conditions. This study is meaningful in that vehicle speed control is performed for the purpose of speed harmonization in a traffic stream based on a comprehensive analysis of inter-vehicle risks. It is expected that the outcome of this study will be valuable for supporting the development of vehicle control systems for preventing crashes in automated driving environments.

[1]  Seolyoung Lee,et al.  Is vehicle automation enough to prevent crashes? Role of traffic operations in automated driving environments for traffic safety. , 2017, Accident; analysis and prevention.

[2]  Chen Wang,et al.  Derivation of a New Surrogate Measure of Crash Severity , 2014 .

[3]  Perry Y. Li,et al.  Traffic flow stability induced by constant time headway policy for adaptive cruise control (ACC) vehicles , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[4]  Deng Pan,et al.  Synchronous Control of Vehicle Following Behavior and Distance Under the Safe and Efficient Steady-Following State: Two Case Studies of High-Speed Train Following Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[5]  Wu Xiaorui,et al.  A Lane Change Model with the Consideration of Car Following Behavior , 2013 .

[6]  Bart Van Arem,et al.  Driver and Vehicle Characteristics and Platoon and Traffic Flow Stability , 2010 .

[7]  Danya Yao,et al.  A Survey of Traffic Control With Vehicular Communications , 2014, IEEE Transactions on Intelligent Transportation Systems.

[8]  Elias B. Kosmatopoulos,et al.  Collision avoidance analysis for lane changing and merging , 1999, IEEE Trans. Veh. Technol..

[9]  Hani S. Mahmassani,et al.  Speed Harmonization , 2013 .

[10]  Cheol Oh,et al.  Real-Time Estimation of Lane Change Risks Based on the Analysis of Individual Vehicle Interactions , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[11]  Chen Wang,et al.  Surrogate Safety Measure for Simulation-Based Conflict Study , 2013 .

[12]  Yanjun Huang,et al.  Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints , 2017, IEEE Transactions on Vehicular Technology.

[13]  Swaroop Darbha,et al.  Intelligent Cruise Control Systems And Traffic Flow Stability , 1998 .

[14]  Xuemin Shen,et al.  Real-Time Path Planning Based on Hybrid-VANET-Enhanced Transportation System , 2015, IEEE Transactions on Vehicular Technology.

[15]  Cheol Oh,et al.  Evaluating the effectiveness of active vehicle safety systems. , 2017, Accident; analysis and prevention.

[16]  Jeremy A. Salinger,et al.  A Unified Approach to Forward and Lane-Change Collision Warning for Driver Assistance and Situational Awareness , 2008 .

[17]  Bart van Arem,et al.  Effects of Cooperative Adaptive Cruise Control on traffic flow stability , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[18]  Karl Heinz Hoffmann,et al.  A mathematical model for predicting lane changes using the steering wheel angle. , 2014, Journal of safety research.

[19]  Gerd Wanielik,et al.  Situation Assessment for Automatic Lane-Change Maneuvers , 2010, IEEE Transactions on Intelligent Transportation Systems.

[20]  Cheol Oh,et al.  Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments , 2019, Transportation Research Part A: Policy and Practice.

[21]  Kyongsu Yi,et al.  Design of Integrated Risk Management-Based Dynamic Driving Control of Automated Vehicles , 2017, IEEE Intelligent Transportation Systems Magazine.