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Improving truck driver and vulnerable road user interactions through driver training: A subject matter expert interview study

Author(s): Galal, Donmez, Roorda Student Poster Competition: 1st Place

Poster Presentation:

Poster link

Abstract:

Background:

Infrequent, truck-vulnerable road user (VRU) crashes are among the most severe road crashes. According to the Federal Motor Carrier Safety Administration (FMCSA), driver error, stemming from recognition and decision errors, is the main factor leading to truck crashes. There have been many research efforts on initiatives to mitigate truck-VRU crashes including infrastructural improvements, vehicle design, and driver warning technologies (1,2). Truck driver training is an effective complementary solution leading to collision reduction and improved driving behaviour (3). However, a number of issues have been raised about current Canadian truck driver training, including insufficient training duration and lack of practical training on actual scenarios (4). Understanding and considering truck drivers’ views about and needs from training and ways to improve it is one important step to inform the advancement of truck driver training and reduce truck collisions.

Aims:

To understand truck drivers’ perceptions of training and the use of a truck simulator as a tool for training in order to identify potential improvements in training. A particular goal is to investigate the potential to incorporate VRU safety and hazard anticipation training, which has been shown to be effective in improving skills of passenger vehicle novice drivers (5).

Methods:

Semi-structured online interviews were conducted with novice and experienced truck drivers (with less and more than 5-years experience, respectively), truck driver trainees (non-licensed), and road safety professionals. Interview transcripts were thematically analysed using NVivo 12. Text elements were grouped into the following themes: conventional training, simulator training, hazard anticipation skills, hazardous scenarios between trucks and VRUs, and truck driver recommendations.

Results:

21 participants (11 novice and 5 experienced truck drivers, 2 trainees, and 3 road safety professionals) were interviewed, out of which 11 participants (61%) used a truck driving simulator during their training. 25% of the licensed truck drivers thought that training was short and left a significant gap between training and real-world practice. The use of a simulator was perceived positively as it provides a safe and relatively realistic driving environment. A missing component in training emerged as VRU safety and the lack of hazard anticipation training. According to a road safety professional, incorporating hazard anticipation training would be effective since it would train truck drivers to anticipate and overcome some of the most frequent hazardous scenarios between trucks and VRUs, like side runovers of VRUs by turning trucks.

Discussion:

The study participants reported that truck driver training could be improved which is in line with previous literature. The use of a truck simulator, incorporating more driving scenarios to training, and more on-road practice during training were strongly endorsed. Hazard anticipation training and a VRU safety component were also encouraged by participants.

Conclusions:

Truck drivers perceive that improvements can be made to conventional training to fully prepare them for real-world driving. Participants endorsed the use of a truck simulator as it provides a safe and more realistic driving environment. A simulator-based hazard anticipation and VRU safety component could be developed and included in training to better prepare truck drivers to anticipate and safely overcome hazardous VRU actions and scenarios.

References:
1. Van Beeck K, Goedeme T. Real-time pedestrian detection in a truck’s blind spot camera BT - 3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014, March 6, 2014 - March 8, 2014. In EAVISE, Campus De Nayer, KU Leuven, J. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, BelgiumESAT-PSI, KU Leuven, Kasteel Arenbergpark 10, 3100 Heverlee, Belgium: SciTePress; 2014. p. 412–20. (ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods). Available from: http://dx.doi.org/10.5220/0004821304120420
2. Pyykonen P, Virtanen A, Kyytinen A. Developing intelligent Blind Spot Detection system for Heavy Goods Vehicles BT - 11th IEEE International Conference on Intelligent Computer Communication and Processing, ICCP 2015, September 3, 2015 - September 5, 2015. In Cluj-Napoca, Romania: Institute of Electrical and Electronics Engineers Inc.; 2015. p. 293–8. (Proceedings - 2015 IEEE 11th International Conference on Intelligent Computer Communication and Processing, ICCP 2015). Available from: http://dx.doi.org/10.1109/ICCP.2015.7312674
3. Kircher K, Ahlstrm C, Ihlstrm J, Ljokkoi T, Culshaw J. Effects of training on truck drivers’ interaction with cyclists in a right turn. Cogn Technol Work. 2020;1–13.
4. Malkin J, Crizzle AM, Zello G, Bigelow P, Shubair M. Long-haul truck driver training does not meet driver needs in Canada. Saf Health Work. 2021;12(1):35–41.
5. McDonald CC, Goodwin AH, Pradhan AK, Romoser MRE, Williams AF. A Review of Hazard Anticipation Training Programs for Young Drivers. J Adolesc Heal [Internet]. 2015;57(1, Supplement):S15–23. Available from: https://www.sciencedirect.com/science/article/pii/S1054139X15000713