Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios

Abstract

Connected and automated vehicles (CAVs) have great potential to improve driving safety. A basic performance evaluation criterion of CAVs is whether they can drive more safely than human drivers in real traffic scenarios. This study proposes a method to optimize longitudinal control model parameters of CAVs using empirical trajectory data of human drivers in risky car-following scenarios. Firstly, the initial car-following pairs (I-CFP) are extracted from empirical trajectory data. Then, two types of real longitudinal control models of CAVs, the adaptive cruise control (ACC) and the cooperative ACC (CACC) control models, are employed for simulation in the car-following scenarios with default parameter values, which generate original trajectories of simulated car-following pairs (S-CFP). Finally, a genetic algorithm (GA) is applied to optimize control model parameters of ACC and CACC vehicles and generate optimized trajectories of car-following pairs (O-CFP). Results indicate that safety condition of S-CFP is better than that of I-CFP, while the O-CFP has the best safety performance. The optimized parameters in the ACC/CACC models are diverse and different from the default parameters, indicating that the best model parameters vary with different car-following scenarios. Findings of this study provide a valuable perspective to reduce the rear-end collision risks.

Keywords

traffic safety / connected and automated vehicle / adaptive cruise control / cooperative adaptive cruise control / longitudinal control model parameters

References

[1] World Health Organization. Global status report on road safety 2018 (Report No. WHO/NMH/NVI/18.20) [R]. 2018. https://www.who.int/publications/i/item/9789241565684.

[2] JeongJ, NamdooK, KarbowskiD, et al. . Implementation of model predictive control into closed-loop micro-traffic simulation for connected automated vehicle. IFAC-Papers Online, 2019, 52(5): 224-230 J]

[3] OroszG. Connected automated vehicle design among human-driven vehicles. IFAC - Papers Online, 2019, 51(34): 403-406 J]

[4] GuoH-y, LiuJ, DaiQ-k, et al. . A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet of Things Journal, 2020, 7(5): 3861-3871 J]

[5] LiY, ChenZ-b, YinY-f, et al. . Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic. Transportation Research Part C: Emerging Technologies, 2020, 111: 496-512 J]

[6] YaoH-d, LiX-p. Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection. Transportation Research Part C: Emerging Technologies, 2020, 121(I): 102846 J]

[7] WangY-y, MasoudN, KhojandiA. Real-time sensor anomaly detection and recovery in connected automated vehicle. IEEE Transactions on Intelligent Transportation Systems, 2021, 2231411-1421 J]

[8] GaoY-h, QuZ-w, JiangJ-l, et al. . Mixed traffic flow signal timing optimization method considering e-bike expansion influence. Journal of Transportation Engineering Part A: Systems, 2021, 147(2): 04020155 J]

[9] KernerB. Effect of autonomous driving on traffic breakdown in mixed traffic flow: A comparison of classical ACC with three-traffic-phase-ACC (TPACC). Physica A: Statistical Mechanics and its Applications, 2021, 562: 125315 J]

[10] SharmaA, ZhengZ-d, KimJ, et al. . Assessing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors. Transportation Research Part C: Emerging Technologies, 2021, 124102934 J]

[11] WangS-y, YuB, WuM-yi. MVCM car-following model for connected vehicles and simulation-based traffic analysis in mixed traffic flow. IEEE Transactions on Intelligent Transportation Systems, 2021, 2365267-5274 J]

[12] LiL-h, GanJ, QuX, et al. . Car-following model based on safety potential field theory under connected and automated vehicle environment. Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2019, 32(12): 76-87[J]

[13] JIANG Luo, JI Jie, REN Yue, et al. Risk modeling and quantification of a platoon in mixed traffic based on the mass-spring-damper model [J]. Journal of Advanced Transportation, 2020: 7475682. DOI: https://doi.org/10.1155/2020/7475682.

[14] ArvinR, KhattakA J, KamraniM, et al. . Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections. Journal of Intelligent Transportation Systems, 2020, 25(2): 170-187 J]

[15] MaJ-q, LeslieE, GhiasiA, et al. . Empirical analysis of a freeway bundled connected-and-automated vehicle application using experimental data. Journal of Transportation Engineering, Part A: Systems, 2020, 146(6): 04020034 J]

[16] MilanésV, ShladoverS E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C: Emerging Technologies, 2014, 48285-300 J]

[17] XiaoL, WangM, VanA B. Realistic car-following models for microscopic simulation of adaptive and cooperative adaptive cruise control vehicles. Transportation Research Record, 2017, 26231-9 J]

[18] XiaoL, WangM, SchakelW, et al. . Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks. Transportation Research Part C: Emerging Technologies, 2018, 96: 380-397 J]

[19] MilanésV, ShladoverS E, SpringJ. Cooperative adaptive cruise control in real traffic situations. IEEE Trans. Intelligent Transportation Systems, 2014, 15(1): 296-305 J]

[20] LiuG-h, WuG-y, JiangH-l, et al. . Safe utilization of chromium-bearing vanadate residue by recovery of vanadium and chromium based on calcium circulation. Journal of Central South University, 2018, 25(10): 2349-2359 J]

[21] YuL-y, WangR-yue. Researches on adaptive cruise control system: a state of the art review. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2022, 236(2–3): 211-240[J]

[22] ChenS-r, ChenFeng. Simulation-based assessment of vehicle safety behavior under hazardous driving conditions. Journal of transportation engineering, 2010, 136(4): 304-315 J]

[23] Mullakkal-BabuF A, WangM, VanA B, et al. . Comparative safety assessment of automated driving strategies at highway merges in mixed traffic. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(4): 3626-3639 J]

[24] WangC, StormsK, WinnerH. Online safety assessment of automated vehicles using silent testing. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(8): 13069-13083 J]

[25] ChenQ-h, GuR-f, HuangH-l, et al. . Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing. Accident Analysis & Prevention, 2021, 151105871 J]

[26] LongK-j, LiW-d, HeS-jian. Research on the differences among vehicle merging behaviors at different service levels. Journal of Changsha University of Science & Technology (Natural Science), 2022, 19(3): 87-95[J]

[27] LiY, GuR-f, LeeJ, et al. . The dynamic tradeoff between safety and efficiency in discretionary lane-changing behavior: A random parameters logit approach with heterogeneity in means and variances. Accident Analysis & Prevention, 2021, 159106271 J]

[28] HuY-p, LiY, HuangH-l, et al. . A high-resolution trajectory data driven method for real-time evaluation of traffic safety. Accident Analysis & Prevention, 2022, 165106503 J]

[29] FHWA. Next generation simulation (NGSIM) [R]. 2006, https://ops.fhwa.dot.gov/traffic analysis tools/ngsim.htm.

[30] LiY, WuD, LeeJ, et al. . Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data. Accident Analysis & Prevention, 2020, 144105676 J]

[31] DengX-dongResearch on MAC protocol of Vehicle Ad Hoc Network based on traffic-aware framework, 2021, Wuhan, Wuhan Institute of Technology[D]

[32] ShiY-t, LiY, CaiQ, et al. . How does heterogeneity affect freeway safety? A simulation-based exploration considering sustainable intelligent connected vehicles. Sustainability, 2020, 21(12): 8941 J]

[33] LiY, WuD, ChenQ, et al. . Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach. Accident Analysis & Prevention, 2021, 159106271 J]

[34] LiY, WangH, WangWei. Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system. Traffic Injury Prevention, 2016, 176597-603 J]

[35] DingS-z, ChenX-m, YuLei. Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles. Journal of Central South University, 2022, 29(5): 1521-1536 J]

[36] YiZ-w, LuW-q, XuL-h, et al. . Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles. Journal of Central South University, 2020, 27(11): 3499-3512 J]