Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
Source abstract
Abstract
Legal requirements for minimum distances between vehicles are often not met for short periods of time, especially when changing lanes on multi-lane roads. These situations are typically non-hazardous, as human drivers anticipate surrounding traffic, allowing for shorter headways and improved traffic flow. Automated vehicles (AVs), however, are typically designed to maintain strict headway limits, potentially reducing traffic efficiency. Therefore, legal questions arise as to whether mandatory gap and headway limits for AVs may be violated during periods of non-compliance. While traffic flow simulation is a common method for analyzing AV impacts, previous studies have typically modeled AV behavior using driver models originally designed to replicate human driving. These models are not well suited for representing clearly defined, structured non-compliant maneuvers, as they cannot simulate intentional, rule-deviating strategies. This paper addresses this gap by introducing a concept for AV non-compliant behavior and implementing it as a module within a pre-existing AV driver model. Simulations were conducted on a three-lane highway with an on-ramp under varying traffic volumes and AV penetration rates. The results showed that, with an AV-penetration rate of more than 25%, road capacity at highway entrances could be increased and travel times reduced by over 20%, provided that AVs were allowed to merge with a legal gap of 0.9 s and a minimum non-compliant gap of 0.6 s lasting up to 3 s. This suggests that performance gains are achievable under adjusted legal requirements. In addition, the proposed framework can serve as a foundation for further development of AV driver models aiming at improving traffic efficiency while maintaining regulatory compliance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Autonomous Vehicle Technology and Safety · Traffic and Road Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Virtual Vehicle Research Center, Inffeldgasse 21a, 8010 Graz, Austria
Institute of Highway Engineering and Transport Planning, Graz University of Technology, Rechbauerstraße 12/II, 8010 Graz, Austria
Funding
FFG, Austrian Research Promotion Agency
References (47)
Show source-provided references
1. Azam, M., Hassan, S.A., and Che Puan, O. (2022). Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis. Sustainability, 14.10.3390/su141710743 ↗
2. Chang, X., Zhang, X., Li, H., Wang, C., and Liu, Z. (2022). A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments. Sustainability, 14.10.3390/su14137629 ↗
3. United Nations Economic and Social Council (2025, July 07). Proposal for the 01 series of amendments to UN Regulation No. 157 (Automated Lane Keeping Systems). ECE/TRANS/WP.29/2022/59/Rev.1, Available online: https://unece.org/sites/default/files/2025-03/ECE_TRANS_WP.29_2022_59_Rev.1e.pdf.
4. Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions10.1016/j.aap.2022.106743 ↗
5. Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice10.1109/TITS.2022.3200176 ↗
6. An Approach for Handling Uncertainties Related to Behaviour and Vehicle Mixes in Traffic Simulation Experiments with Automated Vehicles10.1155/2020/8850591 ↗
7. A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation10.3390/smartcities4010019 ↗
8. Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review10.1155/2019/9343705 ↗
9. Al-Turki, M., Ratrout, N.T., Rahman, S.M., and Reza, I. (2021). Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives. Sustainability, 13.10.3390/su131911052 ↗
10. Wiedemann, R. (1974). Simulation des Straßenverkehrsflusses, Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe.
11. Postigo, I., Olstam, J., and Rydergren, C. (2021, January 28–30). Effects on Traffic Performance Due to Heterogeneity of Automated Vehicles on Motorways: A Microscopic Simulation Study. Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, Online.10.5220/0010450701420151 ↗
12. Schakel, W.J., van Arem, B., and Netten, B.D. (2010, January 19–22). Effects of Cooperative Adaptive Cruise Control on traffic flow stability. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Madeira, Portugal.10.1109/ITSC.2010.5625133 ↗
13. Integrated Lane Change Model with Relaxation and Synchronization10.3141/2316-06 ↗
14. Will Automated Vehicles Negatively Impact Traffic Flow?10.1155/2017/3082781 ↗
15. Congested traffic states in empirical observations and microscopic simulations10.1103/PhysRevE.62.1805 ↗
16. Behrisch, M., and Weber, M. (2015). SUMO’s Lane-Changing Model. Modeling Mobility with Open Data, Springer International Publishing. Lecture Notes in Mobility.10.1007/978-3-319-15024-6 ↗
17. Simulating the Effect of Autonomous Vehicles on Roadway Mobility in a Microscopic Traffic Simulator10.1007/s12239-021-0066-7 ↗
18. A behavioural car-following model for computer simulation10.1016/0191-2615(81)90037-0 ↗
19. Studying Traffic Safety During the Transition Period Between Manual Driving and Autonomous Driving: A Simulation-Based Approach10.1109/TITS.2023.3241970 ↗
20. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data10.1016/j.trc.2014.09.001 ↗
21. Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results10.1109/OJITS.2022.3166394 ↗
22. Zhi, Y., Zhang, Z., Zhou, W., Hou, D., and Zhang, J. (2024). Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways. Appl. Sci., 14.10.3390/app142311137 ↗
23. Porfyri, K.N., Mintsis, E., and Mitsakis, E. (2018). Assessment of ACC and CACC Systems Using SUMO, EPIC Engineering.
24. Simulation of mixed traffic with cooperative lane changes10.1111/mice.12732 ↗
25. Fang, X., Li, H., Tettamanti, T., Eichberger, A., and Fellendorf, M. (2022). Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario. Energies, 15.10.3390/en15062008 ↗
26. Li, H., and Ju, Y. (2025). Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways. Appl. Sci., 15.10.3390/app15031453 ↗
27. Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments10.1016/j.tra.2019.01.010 ↗
28. Cooperative lane control application for fully connected and automated vehicles at multilane freeways10.1016/j.trc.2019.11.007 ↗
29. Variable Speed Release (VSR): Speed Control to Increase Bottleneck Capacity10.1109/TITS.2019.2891314 ↗
30. Microscopic Traffic Flow Simulator VISSIM10.1007/978-1-4419-6142-6_2 ↗
31. Extending Adaptive Cruise Control to Adaptive Driving Strategies10.3141/2000-03 ↗
32. Research on the weaving area capacity of freeways under man–machine mixed traffic flow10.1016/j.physa.2023.129040 ↗
33. SAE-Society of Automotive Engineers (2025, July 07). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Available online: https://saemobilus.sae.org/standards/j3016_202104-taxonomy-definitions-terms-related-driving-automation-systems-road-motor-vehicles.
34. (2018). Intelligent Transport Systems—Adaptive Cruise Control Systems—Performance Requirements and Test Procedures (Standard No. ISO 15622).
35. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions10.1016/j.trc.2015.09.011 ↗
36. Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects10.1016/j.arcontrol.2018.02.001 ↗
37. A survey of motion planning and control techniques for self-driving urban vehicles10.1109/TIV.2016.2578706 ↗
38. Planning and Decision-Making for Autonomous Vehicles10.1146/annurev-control-060117-105157 ↗
39. Hult, R., and Tabar, R. (2013). Path Planning for Highly Automated Vehicles. [Master’s Thesis, Chalmers University of Technology].
40. Rupp, A. (2018). Trajectory Planning and Formation Control for Automated Driving on Highways. [Ph.D. Thesis, Graz University of Technology].
41. Hofinger, F., Mischinger-Rodziewicz, M., Haberl, M., and Fellendorf, M. (2023, January 24–28). Lane Change Model for Automated Vehicles on Multi-Lane Highways in Mixed Traffic. Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain.10.1109/ITSC57777.2023.10421884 ↗
42. Choudhury, C., Ben-Akiva, M.E., Toledo, T., Rao, A., and Lee, G. (2007, January 23–25). State Dependence in Lane Changing Models 1 PAPER: 196 State Dependence in Lane Changing Models. Proceedings of the 17th International Symposium on Transportation and Traffic Theory, London, UK.
43. Calibrating VISSIM for the German Highway Capacity Manual10.3141/2483-09 ↗
44. 3-step calibration process of a microscopic traffic flow simulation platform for mixed traffic scenarios10.1016/j.trpro.2023.11.653 ↗
45. Beyond behavioural change: Investigating alternative explanations for shorter time headways when human drivers follow automated vehicles10.1016/j.trc.2024.104673 ↗
46. Hartmann, M., Motamedidehkordi, N., Krause, S., Hoffmann, S., Vortisch, P., and Busch, F. (2017). Impact of Automated Vehicles on Capacity of the German Freeway Network. ITS World Congress 2017 Compendium of Papers, ITS World Congress.
47. Lytrivis, P., Manganiaris, S., Tötzl, D., Berghäuser, G., Mischinger, M., Rudigier, M., Solmaz, S., Wimmer, Y., Pintsuk, A., and Porcuna, D. (2020). INFRAMIX Deliverable. 5.3: Evaluation, impact analysis and new safety performance criteria. Available online: https://www.inframix.eu/wp-content/uploads/INFRAMIX_D5.3_1.0-final.pdf.