Study on Determining the Ranges of the Inner Wheel Difference Zone for the Right Turns of Large Vehicles
Source abstract
Abstract
To effectively reduce traffic accidents caused by inner wheel differences during large vehicles’ right turns at intersections, this study proposes a quantitative method to calculate the range of inner wheel difference areas. Typical intersections in Nanjing and Taiyuan were selected for field tests using rigid-body and articulated vehicles with different parameters. Dynamic data during turning were collected, revealing a significant linear correlation between the number of steering wheel rotations and the geometric parameters of the intersection as well as the structural dimensions of the vehicle. On the basis of Ackermann’s steering geometry theory, trajectory prediction models for both vehicle types were constructed. Verification with actual trajectory data extracted by Tracker software revealed that the rigid-body vehicle model had a maximum relative error of 8.12% and an average of 5.39%, whereas the articulated vehicle model had a maximum of 8.32% and an average of 5.12%, confirming high model accuracy. A Python-based visualization program for inner wheel difference areas was developed. The results provide technical support for scientifically marking warning areas at intersections and improving the refined management of traffic safety.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic and Road Safety · Traffic control and management · Automotive and Human Injury Biomechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
References (45)
Show source-provided references
1. Effect of restricted sight on Right-Turn driver behavior with pedestrians at signalized intersection10.1155/2014/565394 ↗
2. Inexperience and risky decisions of young adolescents, as pedestriansand cyclists, in interactions with lorries, and the effects of competency versus awareness education10.1016/j.aap.2013.02.038 ↗
3. Zhang, Q., Wei, Y., Wang, K., Liu, H., Xu, Y., and Chen, Y. (2019, January 10–13). Design of Arduino-Based in-vehicle warning device for inner wheel difference. Proceedings of the 2019 IEEE 2nd International Conference on Electronics Technology (ICET), Chengdu, China.10.1109/ELTECH.2019.8839372 ↗
4. Sitao, H., Huihui, X., Qiaojun, X., and Gang, X. (2012, January 24–28). Safety impact analysis of large vehicles’ right-turn on pedestrians and non-motorized vehicle at signalized intersection. Proceedings of the World Automation Congress, Puerto Vallarta, Mexico.
5. Field Testing of a Cyclist Collision Avoidance System for Heavy Goods Vehicles10.1109/TVT.2016.2538801 ↗
6. Malczyk, A., and Bende, J. (2019, January 10–13). Heavy Truck Crashes Involving Pedestrians in Comparison to Bicyclists. Proceedings of the 26th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Technology: Enabling a Safer Tomorrow National Highway Traffic Safety Administration (No. 19-0080), Eindhoven, The Netherlands.
7. Accidents between freight vehicles and bicycles, with a focus on urban areas10.1016/j.trpro.2017.05.474 ↗
8. Conflicts between bikes and trucks in urban areas—A survey of Norwegian cyclists10.1016/j.cstp.2017.11.010 ↗
9. Observations of truck-bicycle encounters: A case study of conflicts and behaviour in Trondheim, Norway10.1016/j.trf.2018.11.018 ↗
10. Zhang, Z. (2021, January 24–26). Collision simulation of trucks and pedestrians based on Pc-Crash in the blind area of truck driving. Proceedings of the 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), Chongqing, China.10.1117/12.2620026 ↗
11. Right-Hook crash scenario: Effects of environmental factors on driver’s visual attention and crash risk10.1061/JTEPBS.0000342 ↗
12. Fatal and serious collisions involving pedal cyclists and trucks in London between 2007 and 201110.1080/15389588.2017.1291938 ↗
13. Wang, T., Zhang, X., Xin, Y., Lou, J., and Yang, W. (2021, January 9–11). The calculation method and the design of the warning system for the dangerous area of the inner wheel difference of the vehicle. Proceedings of the 2021 9th International Conference on Traffic and Logistic Engineering (ICTLE), Macau, China.
14. Electric cyclist injury of the collision between right turn of truck and electric bicycle10.1080/10255842.2021.1892662 ↗
15. Truck-bicycle safety: An overview of methods of study, risk factors and research needs10.1186/s12544-019-0371-7 ↗
16. Factors contributing to bicycle–motorised vehicle collisions: A systematic literature review10.1080/01441647.2017.1314391 ↗
17. Niewoehner, W., and Berg, F.A. (2005, January 6–9). Endangerment of pedestrians and bicyclists at intersections by right turning trucks. Proceedings of the 19th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Washington, DC, USA.
18. A simulator-based analysis of engineering treatments for right-hook bicycle crashes at signalized intersections10.1016/j.aap.2017.04.021 ↗
19. Stochastic approach for modeling the effects of intersection geometry on turning vehicle paths10.1016/j.trc.2012.09.006 ↗
20. Pedestrian-Vehicle accidents reconstruction with PC-Crash®: Sensibility analysis of factors variation10.1016/j.trpro.2016.12.016 ↗
21. Mixed logit analysis of bicyclist injury severity re-sulting from motor vehicle crashes at intersection and non-intersection locations10.1016/j.aap.2010.09.015 ↗
22. COPRAS Decision Model to Optimize Blind Spot in Heavy Vehicles: A Comparative Perspective10.1016/j.proeng.2014.12.383 ↗
23. Research on the optimization design of intersections for safe operation of large trucks
24. Algebraic general trajectory formula for semitrailer truck vehicles10.1109/TASE.2020.3043820 ↗
25. Abramowski, M., Fundowicz, P., Sar, H., Renski, A., and Brukalski, M. (2025). Simulation analysis of the Semi-Trailer steered wheels control algorithm. Sensors, 25.10.3390/s25030626 ↗
26. Assessing right-turning vehicle-pedestrian conflicts at intersections using an integrated microscopic simulation model10.1016/j.aap.2019.05.018 ↗
27. Method for investigation of child occupant impact dynamics based on real-world accident10.1007/s12239-015-0080-8 ↗
28. Freeway accident detection and classification based on the multi-vehicle trajectory data and deep learning model10.1016/j.trc.2021.103303 ↗
29. Comparative analyses of bicyclists and motorcyclists in vehicle collisions focusing on head impact responses10.1177/0954411917723674 ↗
30. A comprehensive assessment of bus rollover crashes: Integration of multibody dynamic and finite element simulation methods10.1080/13588265.2020.1787660 ↗
31. Wang, Q.Z., Sun, J.R., Wang, N.N., Wang, Y., Song, Y., and Li, X. (2022). Exploring the influencing factors and formation of the blind zone of a semitrailer truck in a Right-Turn collision. Sustainability, 14.10.3390/su14169805 ↗
32. Horng, G.J., Huang, Y.C., and Yin, Z.X. (2022). Using bidirectional Long-Term memory neural network for trajectory prediction of large inner wheel routes. Sustainability, 14.10.3390/su14105935 ↗
33. Vision-based measurement and prediction of object trajectory for robotic manipulation in dynamic and uncertain scenarios10.1109/TIM.2020.2994602 ↗
34. Ip, A., Irio, L., and Oliveira, R. (2021, January 25–28). Vehicle trajectory prediction based on LSTM recurrent neural networks. Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland.10.1109/VTC2021-Spring51267.2021.9449038 ↗
35. Evaluation of a radar-based proximity warning system for off-highway dump trucks10.1016/j.aap.2005.07.006 ↗
36. A multimodal ADAS system for unmarked urban scenarios based on road context understanding10.1109/TITS.2014.2368980 ↗
37. Truck drivers’ behavior in encounters with vulnerable road users at intersections: Results from a test-track experiment10.1016/j.aap.2021.106289 ↗
38. Improving truck driver and vulnerable road user interactions through driver training: An interview study with canadian subject matter experts10.1177/03611981231168123 ↗
39. The use of volumetric projections in Digital Human Modelling software for the identification of Large Goods Vehicle blind spots10.1016/j.apergo.2015.10.013 ↗
40. Zhang, R., Liu, J., and Ma, L. (2015, January 18–19). A typical blind spot danger pre-warning method of heavy truck under turning right condition. Proceedings of the 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), Guiyang, China.10.1109/ISDEA.2015.33 ↗
41. Truck drivers’ interaction with cyclists in right-turn situations10.1016/j.aap.2020.105515 ↗
42. Effects of training on truck drivers’ interaction with cyclists in a right turn10.1007/s10111-020-00628-x ↗
43. Liang, Y.H. (2009). Research on Fuzzy Neural Network Prediction Model for Motor-Vehicle and Non-Motor-Vehicle Conflicts at At-Grade Intersections Based on Fault Tree Analysis, Beijing Jiaotong University.10.1061/41064(358)121 ↗
44. Kinematic theoretical model of inner wheel difference during vehicle turning
45. Difference of Radius between Inner Wheels for Vehicles on Urban Road and Safety Countermeasures