Abstract
Since there will be a mix of automated vehicles (AVs) and human-driven vehicles (HVs) on future roadways, in the literature, while many existing studies have investigated collisions where an AV hits an HV from behind, few studies have focused on the scenarios where an HV hits an AV from behind (called HV-AV collision). In this paper, we will investigate the HV-AV collision risk in the Stop-in-Lane (SiL) scenario. To achieve this aim, a Human-like Brake (HLB) model is proposed first to simulate the driver brake control. In particular, the joint distribution of Off-Road-Glance and Time-Headway is originally introduced to simulate the glance distraction of drivers during their dynamic vehicle control. Sequentially, a case study of HV-AV collisions in the SiL scenario of autonomous driving (AD) is conducted based on the HLB model, to reveal how the collision probability changes with respect to various parameters. The results of the case study provide us with an in-depth understanding of the dynamic driving conditions that lead to rear-end collisions in the SiL scenario.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The model of the joint probability distribution \(P_{O,T}(t_i)\) as shown in Eq. (5) is developed jointly with an OEM, which approximates the Gamma distribution. However, we do not show the mathematical representation of \(P_{O,T}(t_i)\) due to the confidentiality of the OEM.
- 2.
The TH introduced to the HLB model only works when the two cars have the same initial speed.
- 3.
The maximum deceleration of a car, in reality, is about 8 m/s2.
References
National Highway Traffic Safety Administration: Summary report: Standing general order on crash reporting for automated driving systems. DOT HS 813, 324 (2022)
Petrović, D., Mijailović, R., Pešić, D.: Traffic accidents with autonomous vehicles: type of collisions, manoeuvres and errors of conventional vehicles’ drivers. Transp. Res. Procedia 45, 161–168 (2020)
Mohammadian, S., Haque, M., Zheng, Z., Bhaskar, A.: Integrating safety into the fundamental relations of freeway traffic flows: a conflict-based safety assessment framework. Anal. Methods Accid. Res. 32, 100187 (2021)
Wang, C., Chen, F., Zhang, Y., Wang, S., Yu, B., Cheng, J.: Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: a random parameter approach with heterogeneity in means and variances. Anal. Methods Accid. Res. 35, 100219 (2022)
Chen, J., Zhang, C., Luo, J., Xie, J., Wan, Y.: Driving maneuvers prediction based autonomous driving control by deep Monte Carlo tree search. IEEE Trans. Veh. Technol. 69(7), 7146–7158 (2020)
Xia, Y., Qu, Z., Sun, Z., Li, Z.: A human-like model to understand surrounding vehicles’ lane changing intentions for autonomous driving. IEEE Trans. Veh. Technol. 70(5), 4178–4189 (2021)
Yoo, J., Langari, R.: A predictive perception model and control strategy for collision-free autonomous driving. IEEE Trans. Intell. Transp. Syst. 20(11), 4078–4091 (2018)
Arbabzadeh, N., Jafari, M.: A data-driven approach for driving safety risk prediction using driver behavior and roadway information data. IEEE Trans. Intell. Transp. Syst. 19(2), 446–460 (2017)
Strickland, M., Fainekos, G., Amor, H.: Deep predictive models for collision risk assessment in autonomous driving. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4685–4692, IEEE (2018)
Muzahid, A., Kamarulzaman, S., Rahim, M.: Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving. In: 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), pp. 1–6, IEEE (2020)
Markkula, G.: Modeling driver control behavior in both routine and near-accident driving. In: The Human Factors and Ergonomics Society Annual Meeting, vol. 58, no. 1, pp. 879–883. SAGE Publications, CA (2014)
Svärd, M., Markkula, G., Engström, J., Granum, F., Bärgman, J.: A quantitative driver model of pre-crash brake onset and control. In: The Human Factors and Ergonomics Society Annual Meeting, vol. 61, no. 1, pp. 339–343. SAGE Publications, CA (2017)
Svärd, M., Markkula, G., Bärgman, J., Victor, T.: Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes. Accid. Anal. Prev. 163, 106433 (2021)
Leroy, J., Gruyer, D., Orfila, O., El Faouzi, N.E.: Five key components based risk indicators ontology for the modelling and identification of critical interaction between human driven and automated vehicles. IFAC-PapersOnLine 53(5), 212–217 (2020)
Leroy, J., Gruyer, D., Orfila, O., El Faouzi, N. E.: Adapted risk indicator for autonomous driving system with uncertainties and multi-dimensional configurations modeling. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 2034–2041, IEEE (2021)
Markkula, G., Benderius, O., Wolff, K., Wahde, M.: A review of near-collision driver behavior models. Hum. Factors 54(6), 1117–1143 (2012)
Markkula, G., Engström, J., Lodin, J., Bärgman, J., Victor, T.: A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accid. Anal. Prev. 95, 209–226 (2016)
Svärd, M., Bärgman, J., Victor, T.: “Detection and response to critical lead vehicle deceleration events with peripheral vision: glance response times are independent of visual eccentricity. Accid. Anal. Prev. 150, 105853 (2021)
Markkula, G., Boer, E., Romano, R., Merat, N.: Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering. Biol. Cybern. 112(3), 181–207 (2018)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Liang, C., Ghazel, M., Cazier, O., El-Koursi, E.M: Developing accident prediction model for railway level crossings. Saf. Sci. 101, 48–59 (2018)
SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE J3016_202104 (2021)
ISO 21448:2022 (E): Road vehicles - Safety of the intended functionality (2022)
Acknowledgement
This work was supported by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project)(2022JBXT003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liang, C., Ghazel, M., Ci, Y., Faouzi, NE.E., Wang, R., Zheng, W. (2023). Rear-End Collision Risk Analysis for Autonomous Driving. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-40953-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40952-3
Online ISBN: 978-3-031-40953-0
eBook Packages: Computer ScienceComputer Science (R0)