Abstract
The development and deployment of autonomous driving (AD) systems are very challenging from a safety perspective. In addition to the main AD processing pipeline, self-driving cars need a safety assessment system that can evaluate the degree of safety of the ego vehicle with respect to its environment at run time. As early risk detection is very important in case of a dangerous situation or a system failure, the safety assessment is required be “predictive”, i.e. the evaluated degree of safety needs to not only consider the current state but also anticipate the dynamic evolution of the road traffic. In this work, we present a predictive safety assessment system for level 3 autonomous driving by assuming certain motion models for the ego vehicle and the other road users. Firstly we formulate both longitudinal and lateral safety as constrained optimization problems. Then we propose dynamic programming (DP) algorithms to solve these problems in real time. The DP algorithm for longitudinal safety solves the problem exactly by taking advantage of the special properties of the problem, whereas the DP algorithm for lateral safety solves the problem approximately by employing efficient approximate control policies. Experimental results on simulated driving scenarios demonstrated the effectiveness of the proposed safety assessment system.
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
Similar content being viewed by others
References
Yurtsever, E., Lambert, J., Carballo, A., Takeda, K.: A survey of autonomous driving: common practices and emerging technologies. IEEE Access 8, 58443–58469 (2020)
Avizienis, A., Laprie, J.C., Randell, B., Landwehr, C.E.: Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Inf. Forensics Secur. (TIFS) 1(1), 11–33 (2004)
ISO: Road vehicles – functional safety (ISO 26262) (2011)
Reschka, A.: Safety concept for autonomous vehicles. In: Maurer, M., Gerdes, J.C., Lenz, B., Winner, H. (eds.) Autonomous Driving, pp. 473–496. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-48847-8_23
Machin, M., Dufossé, F., Blanquart, J.-P., Guiochet, J., Powell, D., Waeselynck, H.: Specifying safety monitors for autonomous systems using model-checking. In: Bondavalli, A., Di Giandomenico, F. (eds.) SAFECOMP 2014. LNCS, vol. 8666, pp. 262–277. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10506-2_18
Machin, M., Guiochet, J., Waeselynck, H., Blanquart, J.P., Roy, M., Masson, L.: SMOF - A Safety MOnitoring Framework for autonomous systems. IEEE Trans. Syst. Man Cybern. Syst. 48(5), 702–715 (2018)
Haupt, N.B., Liggesmeyer, P.: Systematic specification of a service safety monitor for autonomous vehicles. In: International Workshop on Critical Automotive Applications: Robustness & Safety (2019)
Mekki-Mokhtar, A., Blanquart, J.P., Guiochet, J., Powell, D., Roy, M.: Safety trigger conditions for critical autonomous systems. In: IEEE Pacific Rim International Symposium on Dependable Computing (PRDC) (2012)
Shalev-Shwartz, S., Shammah, S., Shashua, A.: On a formal model of safe and scalable self-driving cars (2018)
Schlatow, J., et al.: Self-awareness in autonomous automotive systems. In: IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1050–1055 (2017)
SAE: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Standard, SAE International, April 2021
Rajamani, R.: Vehicle Dynamics and Control, 2nd edn. Springer, Cham (2012). https://doi.org/10.1007/978-1-4614-1433-9
Bertsekas, D.P.: Dynamic Programming and Optimal Control, 4th edn., vol. I. Athena Scientific (2017)
Frank, S.A.: Control Theory Tutorial: Basic Concepts Illustrated by Software Examples. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91707-8
Wright, S.J.: Coordinate descent algorithms. Math. Program. 151(1), 3–34 (2015). https://doi.org/10.1007/s10107-015-0892-3
Dulac, N., Delaunay, C., Michel, G.: Real-time, multisensor, advanced prototyping software. In: First National Workshop on Control Architectures of Robots (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Qin, L., Benmokhtar, R., Perrotton, X. (2022). Real-Time Model Predictive Safety Assessment for Level 3 Autonomous Driving. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-97672-9_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97671-2
Online ISBN: 978-3-030-97672-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)