Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds
Abstract
:1. Introduction
2. Safety Metrics
State parameters | Description |
---|---|
Velocity of the ego vehicle | |
Velocity of the target vehicle | |
Relative velocity between the ego vehicle and the target vehicle in front | |
Current longitudinal acceleration of the ego vehicle | |
Maximum longitudinal deceleration | |
Maximum lateral acceleration | |
Required longitudinal deceleration of the ego vehicle | |
Required lateral deceleration of the ego vehicle | |
Time parameters | |
Time to reach LPTB (TTB ) | |
Time to reach LPTS (TTS ) | |
Threshold for the | |
Threshold for the | |
Response time of the ego vehicle | |
Evasion time needed to change a lane | |
Distance parameters | |
Initial distance between the ego and target vehicle at the point t=0 | |
Distance to target vehicle at | |
Distance to target vehicle at | |
Lateral evasion distance | |
Minimum safety distance for braking maneuver | |
Minimum safety distance for steering maneuver | |
Remaining parameters | |
g | Gravitational constant |
Road friction coefficient |
3. Assessment Method
3.1. Concept of Fictive Vehicles
3.2. Definition of the Criticality
3.2.1. Definition of Variable Criticality Thresholds
3.2.2. Criticality Scale
- Longitudinal direction
- Lateral direction
- TTB ≥ TTS
- TTB < TTS
3.3. Criticality Assessment Method
4. Simulation Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ADS | automated driving systems |
SCS | safety-critical scenario |
TTC | time to collision |
TTR | time to react |
TTB | time to brake |
TTS | time to steer |
TTK | time to kickdown |
TTx | time to x |
LPTB | last point to brake |
LPTS | last point to steer |
VCT | variable criticality threshold |
References
- Feng, S.; Feng, Y.; Sun, H.; Zhang, Y.; Liu, H.X. Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework. IEEE Trans. Intell. Transp. Syst. 2020, 23, 1213–1222. [Google Scholar] [CrossRef]
- Neurohr, C.; Westhofen, L.; Henning, T.; de Graaff, T.; Möhlmann, E.; Böde, E. Fundamental Considerations around Scenario-Based Testing for Automated Driving. In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, 19 October–13 November 2020; pp. 121–127. [Google Scholar] [CrossRef]
- Nalic, D.; Mihalj, T.; Bäumler, M.; Lehmann, M.; Eichberger, A.; Bernsteiner, S. Scenario Based Testing of Automated Driving Systems: A Literature Survey. In Proceedings of the FISITA Web Congress 2020, Virtual Event, 24 November 2020. [Google Scholar]
- Wen, M.; Park, J.; Cho, K. A scenario generation pipeline for autonomous vehicle simulators. Hum.-Centric Comput. Inf. Sci. 2020, 10, 24. [Google Scholar] [CrossRef]
- Andreotti, E.; Boyraz, P.; Selpi. Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations. IEEE Trans. Intell. Veh. 2020, 6, 366–375. [Google Scholar] [CrossRef]
- Leitner, A. ENABLE-S3: Project Introduction. In Validation and Verification of Automated Systems; Springer: Cham, Switzerland, 2020; pp. 13–23. [Google Scholar]
- Rodemerk, C.; Habenicht, S.; Weitzel, A.; Winner, H.; Schmitt, T. Development of a general criticality criterion for the risk estimation of driving situations and its application to a maneuver-based lane change assistance system. In Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Madrid, Spain, 3–7 June 2012; pp. 264–269. [Google Scholar] [CrossRef]
- Stumper, D.; Knapp, A.; Pohl, M.; Dietmayer, K.C.J. Towards Characterization of Driving Situations via Episode-Generating Polynomials. In Advanced Microsystems for Automotive Applications 2016; Springer: Cham, Switzerland, 2016; pp. 165–173. [Google Scholar]
- Riedmaier, S.; Ponn, T.; Ludwig, D.; Schick, B.; Diermeyer, F. Survey on Scenario-Based Safety Assessment of Automated Vehicles. IEEE Access 2020, 8, 87456–87477. [Google Scholar] [CrossRef]
- Broadhurst, A.; Baker, S.; Kanade, T. Monte Carlo road safety reasoning. In Proceedings of the IEEE Proceedings. Intelligent Vehicles Symposium, Las Vegas, NV, USA, 6–8 June 2005; pp. 319–324. [Google Scholar]
- Mahmud, S.S.; Ferreira, L.; Hoque, M.S.; Tavassoli, A. Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs. IATSS Res. 2017, 41, 153–163. [Google Scholar] [CrossRef]
- Li, Y.; Zheng, Y.; Morys, B.; Pan, S.; Wang, J.; Li, K. Threat Assessment Techniques in Intelligent Vehicles: A Comparative Survey. IEEE Intell. Transp. Syst. Mag. 2020, 13, 71–91. [Google Scholar] [CrossRef]
- Ozbay, K.; Yang, H.; Bartin, B.; Mudigonda, S. Derivation and validation of new simulation-based surrogate safety measure. Transp. Res. Rec. 2008, 2083, 105–113. [Google Scholar] [CrossRef] [Green Version]
- Wachenfeld, W.; Junietz, P.; Wenzel, R.; Winner, H. The worst-time-to-collision metric for situation identification. In Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19–22 June 2016; pp. 729–734. [Google Scholar] [CrossRef]
- Minderhoud, M.M.; Bovy, P.H. Extended time-to-collision measures for road traffic safety assessment. Accid. Anal. Prev. 2001, 33, 89–97. [Google Scholar] [CrossRef]
- Hillenbrand, J. Fahrerassistenz zur Kollisionsvermeidung. Ph.D. Thesis, Universität Karlsruhe (TH), Karlsruhe, Germany, 2007. [Google Scholar] [CrossRef]
- Junietz, P.M. Microscopic and Macroscopic Risk Metrics for the Safety Validation of Automated Driving. Ph.D. Thesis, Technische Universität, Darmstadt, Germany, 2019. [Google Scholar]
- Van Der Horst, R.; Hogema, J. Time-to-collision and collision avoidance systems. In Proceedings of the 6th ICTCT WorkshopSafety Evaluation of Traffic Systems: Traffic Conflicts and Other Measures, Salzburg, Austria, 27–29 October 1993; pp. 109–121. [Google Scholar]
- Chen, R.; Sherony, R.; Gabler, H.C. Comparison of Time to Collision and Enhanced Time to Collision at Brake Application during Normal Driving. In Proceedings of the SAE 2016 World Congress and Exhibition. SAE International, Detroit, MI, USA, 12–14 April 2016. [Google Scholar] [CrossRef]
- Hillenbrand, J.; Kroschel, K.; Schmid, V. Situation assessment algorithm for a collision prevention assistant. In Proceedings of the IEEE Proceedings. Intelligent Vehicles Symposium 2005, Las Vegas, NV, USA, 6–8 June 2005; pp. 459–465. [Google Scholar] [CrossRef]
- Eckert, A.; Hartmann, B.; Sevenich, M.; Rieth, P. Emergency steer & brake assist: A systematic approach for system integration of two complementary driver assistance systems. In Proceedings of the 22nd International Technical Conference on the Enhanced Safety of Vehicles (ESV), Washington, DC, USA, 13–16 June 2011; pp. 13–16. [Google Scholar]
- Winner, H.; Lemmer, K.; Form, T.; Mazzega, J. Pegasus—First steps for the safe introduction of automated driving. In Road Vehicle Automation 5; Springer: Cham, Switzerland, 2019; pp. 185–195. [Google Scholar]
- Weber, H.; Bock, J.; Klimke, J.; Roesener, C.; Hiller, J.; Krajewski, R.; Zlocki, A.; Eckstein, L. A framework for definition of logical scenarios for safety assurance of automated driving. Traffic Inj. Prev. 2019, 20, S65–S70. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shalev-Shwartz, S.; Shammah, S.; Shashua, A. On a formal model of safe and scalable self-driving cars. arXiv 2017, arXiv:1708.06374. [Google Scholar]
- Nguyen, T.; NguyenDinh, N.; Lechner, B.; Wong, Y.D. Insight into the lateral ride discomfort thresholds of young-adult bus passengers at multiple postures: Case of Singapore. Case Stud. Transp. Policy 2019, 7, 617–627. [Google Scholar] [CrossRef]
- Hoberock, L.L. A survey of longitudinal acceleration comfort studies in ground transportation vehicles. J. Dyn. Syst. Meas. Control 1977, 99, 76–84. [Google Scholar] [CrossRef]
- Maurer, M.; Eskandarian, A. Forward collision warning and avoidance. In Handbook of Intelligent Vehicles; Springer: London, UK, 2012; pp. 657–687. [Google Scholar]
- Rogic, B.; Nalic, D.; Eichberger, A.; Bernsteiner, S. A novel approach to integrate human-in-the-loop testing in the development chain of automated driving: The example of automated lane change. IFAC-PapersOnLine 2020, 53, 10188–10195. [Google Scholar] [CrossRef]
- Yang, M.; Wang, X.; Quddus, M. Examining lane change gap acceptance, duration and impact using naturalistic driving data. Transp. Res. Part C Emerg. Technol. 2019, 104, 317–331. [Google Scholar] [CrossRef]
Acceleration Levels | ||
---|---|---|
Comfort | ||
Intermediate 1 | ||
Intermediate 2 | ||
Emergency |
Scenario Description | ||||
---|---|---|---|---|
Start position on the lane in m | Start velocity in km/h | Speed reduction time in s | Desired speed reduction in km/h | |
Scenario 1 | Trailing vehicle is not considered | |||
EGO | 50 | 130 | None | None |
250 | 105 | 15 | 90 | |
250 | 100 | 15 | 50 | |
250 | 105 | 15 | 70 | |
Scenario 2 | Trailing vehicle is considered. | |||
Trailing vehicle | 0 | 130 | 15 | 100 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Nalic, D.; Mihalj, T.; Orucevic, F.; Schabauer, M.; Lex, C.; Sinz, W.; Eichberger, A. Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds. Sensors 2022, 22, 8780. https://doi.org/10.3390/s22228780
Nalic D, Mihalj T, Orucevic F, Schabauer M, Lex C, Sinz W, Eichberger A. Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds. Sensors. 2022; 22(22):8780. https://doi.org/10.3390/s22228780
Chicago/Turabian StyleNalic, Demin, Tomislav Mihalj, Faris Orucevic, Martin Schabauer, Cornelia Lex, Wolfgang Sinz, and Arno Eichberger. 2022. "Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds" Sensors 22, no. 22: 8780. https://doi.org/10.3390/s22228780