Beyond Time to Collision: The Point of No Return as a Reliable Safety Indicator in Rear-End Vehicle Conflicts
Source abstract
Abstract
This paper introduces the concept of the Point of No Return as a physically grounded safety indicator for rear-end vehicle conflicts, addressing fundamental limitations of the widely used time-to-collision metric. Unlike purely kinematic approaches, the proposed formulation incorporates braking capability and reaction constraints, enabling a direct assessment of whether a collision can still be avoided. To illustrate the applicability of the concept, a vision-based framework using a single camera is developed based on dashcam data, combining YOLO-based object detection, Kalman-filter tracking, and geometric distance estimation derived from bounding-box features and camera projection models. The estimated distance is further processed to obtain relative motion, allowing a unified analysis of time to collision and the Point of No Return within the same evaluation pipeline. Experimental results on real-world driving sequences show that the Point of No Return consistently precedes critical conditions identified by time to collision and provides a more stable and physically interpretable characterization of the transition toward collision inevitability. The results also highlight the sensitivity of the proposed indicator to braking capability, while showing lower sensitivity to variations in relative speed. Overall, this study demonstrates the relevance of the Point of No Return as a complementary indicator for collision risk assessment, offering a physically meaningful basis for decision-making in driver assistance systems and improving the interpretation of critical traffic situations. The proposed approach supports sustainable urban mobility by enabling earlier and more reliable intervention strategies, contributing to improved traffic safety, smoother traffic flow, and reduced environmental impact.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Traffic and Road Safety · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Automotive and Transport Engineering, Transilvania University of Brasov, 500036 Brasov, Romania
References (47)
Show source-provided references
1. A Systematic Review of Autonomous Emergency Braking System: Impact Factor, Technology, and Performance Evaluation10.1155/2022/1188089 ↗
2. Drivers’ Estimates of Time to Collision10.1016/0001-4575(94)90042-6 ↗
3. Comparing Time to Collision and Time Headway as Safety Criteria10.5505/pajes.2020.79837 ↗
4. Auditory Information Improves Time-to-Collision Estimation for Accelerating Vehicles10.1007/s12144-022-03375-6 ↗
5. Analysis of the Transition Condition of Rear-End Collisions Using Time-to-Collision Index and Vehicle Trajectory Data10.1016/j.aap.2020.105676 ↗
6. (2026, March 23). Automatic Emergency Braking Using a Time-to-Collision Threshold Based on Target Acceleration. Available online: https://patents.justia.com/patent/11724673.
7. A Novel Algorithm for Crash Detection Under General Road Scenes Using Crash Probabilities and an Interactive Multiple Model Particle Filter10.1109/TITS.2014.2320447 ↗
8. Meléndez-Useros, M., Jiménez-Salas, M., Viadero-Monasterio, F., and Boada, B.L. (2023). Tire Slip H∞ Control for Optimal Braking Depending on Road Condition. Sensors, 23.10.3390/s23031417 ↗
9. Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems10.1109/ACCESS.2020.2965644 ↗
10. A Wheel Slip Ratio Constraint Control for ABS Based on Tangent Type Barrier Lyapunov Function10.1177/00202940221126177 ↗
11. Crocetti, F., Costante, G., Fravolini, M.L., and Valigi, P. (2020). A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions. Proceedings of the 2020 28th Mediterranean Conference on Control and Automation (MED), Saint-Raphaël, France, 15–18 September 2020, IEEE.10.1109/MED48518.2020.9182792 ↗
12. Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System10.1155/2014/923632 ↗
13. Vehicle Distance Estimation Using a Mono-Camera for FCW/AEB Systems10.1007/s12239-016-0050-9 ↗
14. Dellaert, F., and Thorpe, C. (1997, January 9–12). Robust Car Tracking Using Kalman filtering and Bayesian Templates. Proceedings of the 1997 Conference on Intelligent Transportation Systems, Boston, MA, USA.
15. Gao, J., Han, G., Zhu, H., and Liao, L. (2024). Multiple Moving Vehicles Tracking Algorithm with Attention Mechanism and Motion Model. Electronics, 13.10.3390/electronics13010242 ↗
16. Optimized Object Tracking Technique Using Kalman Filter10.14203/j.mev.2016.v7.57-66 ↗
17. Lee, S., Han, K., Park, S., and Yang, X. (2022). Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems. Symmetry, 14.10.3390/sym14122657 ↗
18. Megalingam, R.K., Shriram, V., Likhith, B., Rajesh, G., and Ghanta, S. Monocular Distance Estimation Using Pinhole Camera Approximation to Avoid Vehicle Crash and Back-over Accidents. Proceedings of the 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India, 7–8 January 2016, IEEE.10.1109/ISCO.2016.7727017 ↗
19. Agand, P., Chang, M., and Chen, M. (2024). DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information. Proceedings of the 2024 13th International Workshop on Robot Motion and Control (RoMoCo), IEEE.10.1109/RoMoCo60539.2024.10604404 ↗
20. Event-Triggered Add-on Safety for Connected and Automated Vehicles Using Road-Side Network Infrastructure
21. Yimer, T.H., Wen, C., Yu, X., and Jiang, C. (2020). A Study of the Minimum Safe Distance between Human Driven and Driverless Cars Using Safe Distance Model. arXiv.
22. Near-Miss Determination Through Use of a Scale of Danger
23. Lai, F., Liu, J., and Hu, Y. (2024). An Automatic Emergency Braking Control Method for Improving Ride Comfort. World Electr. Veh. J., 15.10.3390/wevj15060259 ↗
24. Vehicle Forward Collision Warning Algorithm Based on Road Friction10.1016/j.trd.2018.04.017 ↗
25. Pak, J.M. (2022). Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems. Sensors, 22.10.3390/s22030875 ↗
26. Evaluation of Comfort Zone Boundary Based Automated Emergency Braking Algorithms for Car-to-powered-two-wheeler Crashes in China10.1049/itr2.12532 ↗
27. Shalev-Shwartz, S., Shammah, S., and Shashua, A. (2018). On a Formal Model of Safe and Scalable Self-Driving Cars. arXiv.
28. Spitzhüttl, F., and Liers, H. (2019, January 10–13). Calculation of the Point of No Return (PONR) from Real-World Accidents. Proceedings of the 26th International Technical Conference on the Enhanced Safety of Vehicles (ESV): Technology: Enabling a Safer Tomorrow, National Highway Traffic Safety Administration, Eindhoven, The Netherlands.
29. Ma, X., Yu, Q., and Liu, J. (2022). Modeling Urban Freeway Rear-End Collision Risk Using Machine Learning Algorithms. Sustainability, 14.10.3390/su141912047 ↗
30. (2026, March 23). What Is Camera Calibration?. Available online: https://www.mathworks.com/help/vision/ug/camera-calibration.html.
31. (2026, March 23). Camera Calibration and 3D Reconstruction. Available online: https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html.
32. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
33. Zhang, A., Lipton, Z.C., Li, M., and Smola, A.J. (2023). Dive into Deep Learning, Cambridge University Press. Online Book.
34. A Comprehensive Review on YOLO Versions for Object Detection
35. Liang, H., Ma, Z., and Zhang, Q. (2022). Self-Supervised Object Distance Estimation Using a Monocular Camera. Sensors, 22.10.3390/s22082936 ↗
36. Shi, X., Ye, Q., Chen, X., Chen, C., Chen, Z., and Kim, T.-K. (2022, January 18–24). Geometry-Based Distance Decomposition for Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision, New Orleans, LA, USA.10.1109/ICCV48922.2021.01489 ↗
37. Elsagheer Mohamed, S.A., Alshalfan, K.A., Al-Hagery, M.A., and Ben Othman, M.T. (2022). Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles. Sensors, 22.10.3390/s22187051 ↗
38. Analysis of Stopping Sight Distance (SSD) Parameters: A Review Study10.1016/j.procs.2022.03.019 ↗
39. Meywerk, M. (2015). Vehicle Dynamics, John Wiley & Sons Ltd.
40. Estimated Benefit of Automated Emergency Braking Systems for Vehicle–Pedestrian Crashes in the United States10.1080/15389588.2019.1602729 ↗
41. Risk Evaluation of Multiple Passing Maneuvers on Two-Lane Rural Highways in Iran10.1016/j.aap.2021.106472 ↗
42. Zimmermann, J., Mönnich, J., Scherl, M., Llatser, I., Wildschütte, F., and Hofmann, F. (2025). Analyzing the Performance of a V2X-Enhanced Braking System in Real-World Crash Situations. Proceedings of the 2025 IEEE Vehicular Networking Conference (VNC), Porto, Portugal, 2–4 June 2025, IEEE.10.1109/VNC64509.2025.11054146 ↗
43. Wagner, S., Groh, K., Kuhbeck, T., Dorfel, M., and Knoll, A. (2018). Using Time-to-React Based on Naturalistic Traffic Object Behavior for Scenario-Based Risk Assessment of Automated Driving. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, 26–30 June 2018, IEEE.10.1109/IVS.2018.8500624 ↗
44. (2026, March 23). Road Friction Estimation: Why Real-Time Edge Computing Is Redefining Autonomous Vehicle Safety in 2026. Available online: https://www.easyrain.it/road-friction-estimation/.
45. A Two-Dimensional Surrogate Safety Measure Based on Fuzzy Logic Model10.1016/j.aap.2024.107529 ↗
46. Adaptive Forward Collision Warning System for Hazmat Truck Drivers: Considering Differential Driving Behavior and Risk Levels10.1016/j.aap.2023.107221 ↗
47. Understanding the Impacts of Negative Advanced Driving Assistance System Warnings on Hazardous Materials Truck Drivers’ Responses Using Interpretable Machine Learning10.1016/j.engappai.2025.110308 ↗