Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance
Source abstract
Abstract
Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human-Automation Interaction and Safety · Transportation and Mobility Innovations · Older Adults Driving Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering, STEM College, RMIT University, P.O. Box 2476, Melbourne, VIC 3001, Australia
Law and Technology Group, Law School, La Trobe University, Melbourne, VIC 3086, Australia
References (60)
Show source-provided references
1. World Health Organization (2023, July 15). Global Status Report on Road Safety 2018, Switzerland, 2018. Available online: https://apps.who.int/iris/handle/10665/276462.
2. Australian Road Safety Foundation (2023, July 13). Expansion of the RoadSet Program, Australia, Available online: https://treasury.gov.au/sites/default/files/2022-03/258735_australian_road_safety_foundation.pdf.
3. Commonwealth of Australia (2023, July 13). Road Trauma Australia Annual Summaries. Australian Government Department of Infrastructure, Transport, Regional Development and Communications, Australia, Available online: https://www.bitre.gov.au/sites/default/files/documents/road_trauma_australia_2019_statistical_summary.pdf.
4. NRSPP Australia (2023, September 10). “Human Error in Road Accidents.” Monas University Accident Research Centre. Available online: https://www.nrspp.org.au/resources/human-error-in-road-accidents/.
5. Speedometer monitoring when driving with a speed warning system10.1186/s12544-020-00408-8 ↗
6. Measured increases in steering entropy may predict when performance will degrade: A driving simulator study10.1016/j.trf.2022.10.006 ↗
7. Kettwich, C., Schrank, A., Avsar, H., and Oehl, M. (2022). A Helping Human Hand: Relevant Scenarios for the Remote Operation of Highly Automated Vehicles in Public Transport. Appl. Sci., 12.10.3390/app12094350 ↗
8. Woo, H., Madokoro, H., Sato, K., Tamura, Y., Yamashita, A., and Asama, H. (2019). Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction. Appl. Sci., 9.10.3390/app9224875 ↗
9. Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles10.1109/JPROC.2006.888388 ↗
10. Toyota drivers’ experiences with Dynamic Radar Cruise Control, Pre-Collision System, and Lane-Keeping Assist10.1016/j.jsr.2015.12.002 ↗
11. Murphey, Y.L., Kolmanovsky, I., and Watta, P. (2022). AI-Enabled Technologies for Autonomous and Connected Vehicles, Springer Nature.10.1007/978-3-031-06780-8 ↗
12. Alanazi, F. (2023). A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management. Appl. Sci., 13.10.3390/app13031789 ↗
13. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations10.1016/j.tra.2015.04.003 ↗
14. Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape10.1016/j.trf.2023.09.013 ↗
15. Should I take over? Does system knowledge help drivers in making take-over decisions while driving a partially automated car?10.1016/j.trf.2018.11.016 ↗
16. Kim, S., Oh, J., Seong, M., Jeon, E., Moon, Y.-K., and Kim, S. (2023). Assessing the Impact of AR HUDs and Risk Level on User Experience in Self-Driving Cars: Results from a Realistic Driving Simulation. Appl. Sci., 13.10.3390/app13084952 ↗
17. Rui, Y., Wang, S., Wu, R., and Shen, Z. (2023). Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways. Appl. Sci., 13.10.3390/app13064072 ↗
18. Taplin, T., Harrison, K., and Robinson, C. (2023, June 18). Regulation of Autonomous Vehicles, California, Available online: https://berkeleyca.gov/sites/default/files/documents/2022-10-11%20Item%2015%20Regulation%20of%20Autonomous%20Vehicles.pdf.
19. Kim, W., Yang, H., and Kim, J. (2023). Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles. Appl. Sci., 13.10.3390/app13106147 ↗
20. The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles10.1016/j.trf.2021.08.010 ↗
21. A Review of Truck Platooning Projects for Energy Savings10.1109/TIV.2016.2577499 ↗
22. Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy10.5198/jtlu.2019.1405 ↗
23. Murtaza, M., Cheng, C.-T., Fard, M., and Zeleznikow, J. (2022, January 3–5). Supporting Driver Training—From Vehicles with Advanced Driver Assistance Systems to Fully Autonomous Vehicles. Proceedings of the Autonomous Vehicle Technology conference-APAC21, Melbourne, Australia. Available online: https://www.fisita.com/library/apac-21-136.
24. The importance of transparency in naming conventions, designs, and operations of safety features: From modern ADAS to fully autonomous driving functions10.1007/s00146-022-01442-x ↗
25. Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel10.1177/0018720818788164 ↗
26. What does an Automated Vehicle class as a hazard? Using online video-based training to improve drivers’ trust and mental models for activating an Automated Vehicle10.1016/j.trf.2023.08.005 ↗
27. JGaspar, G., Carney, C., Shull, E., and Horrey, W.J. (2023, July 13). The Impact of Driver’s Mental Models of Advanced Vehicle Technologies on Safety and Performance [Supporting Datasets], Available online: https://rosap.ntl.bts.gov/view/dot/56626.
28. Advanced vehicle technology: Mapping mental model accuracy and system exposure to driver behavior10.1177/1071181321651086 ↗
29. Exploring the factors influencing acquisition and learning experiences of cars fitted with advanced driver assistance systems (ADAS)10.1016/j.trf.2023.02.006 ↗
30. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, January 13–15). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, CA, USA.
31. Nwadiuto, J.C., Okuda, H., and Suzuki, T. (2021). Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model. Appl. Sci., 11.10.3390/app11114938 ↗
32. CarSim (2024, February 15). Car Sim Mechanical Simulation. Available online: https://www.carsim.com/.
33. Allen, M. (2024, February 15). Vehicle Dynamics International. Available online: https://www.vehicledynamicsinternational.com/.
34. Is text-based user manual enough? A driving simulator study of three training paradigms for conditionally automated driving10.1016/j.trf.2023.05.006 ↗
35. Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures10.1016/j.apergo.2019.103036 ↗
36. How are car buyers and car sellers currently informed about ADAS? An investigation among drivers and car sellers in the Netherlands
37. Advanced driver assistance systems (ADAS): Demographics, preferred sources of information, and accuracy of ADAS knowledge10.1016/j.trf.2021.08.006 ↗
38. Learning about advanced driver assistance systems—The case of ACC and FCW in a sample of Czech drivers10.1016/j.trf.2018.05.032 ↗
39. A new approach for Training Needs Analysis: A case study using an Automated Vehicle10.1016/j.apergo.2023.104014 ↗
40. The Usability of Print and Online Video Instructions10.1080/10572252.2013.775628 ↗
41. Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial10.2147/AMEP.S62473 ↗
42. Using Video and Paper–Based Educational Resources to Teach Common Surgical Techniques to Pre–Clerkship Medical Students: Results from a Simulation–Based Training Workshop
43. Screencast Tutorials Enhance Student Learning of Statistics10.1177/0098628311430640 ↗
44. Nimmerichter, A., Weber, N.J.R., Wirth, K., and Haller, A. (2015). Effects of Video-Based Visual Training on Decision-Making and Reactive Agility in Adolescent Football Players. Sports, 4.10.3390/sports4010001 ↗
45. Video Improves Learning in Higher Education: A Systematic Review10.3102/0034654321990713 ↗
46. Ragazou, V., and Karasavvidis, I. (2023). Effects of Signaling and Practice Types in Video-Based Software Training. Educ. Sci., 13.10.3390/educsci13060602 ↗
47. A comparison of paper-based and video tutorials for software learning10.1016/j.compedu.2014.06.003 ↗
48. The promise of multimedia learning: Using the same instructional design methods across different media10.1016/S0959-4752(02)00016-6 ↗
49. SAE International (2021). SAE Levels of Driving Automation™ Refined for Clarity and International Audience, SAE International. Available online: https://www.sae.org/blog/sae-j3016-update.
50. Kelechava, B. (2024, February 15). SAE Levels of Driving Automation; American National Standard Institute: Washington, DC, USA. Available online: https://blog.ansi.org/sae-levels-driving-automation-j-3016-2021/.
51. Human-like driving behaviour emerges from a risk-based driver model10.1038/s41467-020-18353-4 ↗
52. Commonwealth of Massachusetts (2023, November 25). Safe Driver Insurance Plan (SDIP) and Your Auto Insurance Policy, Available online: https://www.mass.gov/info-details/safe-driver-insurance-plan-sdip-and-your-auto-insurance-policy#:~:text=An%20experienced%20driver%20is%20someone,recognized%20for%20Basic%20insurance%20discounts.
53. How does drivers’ visual search change as a function of experience? A systematic review and meta-analysis10.1016/j.aap.2019.105266 ↗
54. How many words do we read per minute? A review and meta-analysis of reading rate10.1016/j.jml.2019.104047 ↗
55. Training for the safe activation of Automated Vehicles matters: Revealing the benefits of online training to creating glaringly better mental models and behaviour10.1016/j.apergo.2023.104057 ↗
56. Shapiro, S.S., and Wilk, M.B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52.10.2307/2333709 ↗
57. Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes10.1214/aoms/1177729437 ↗
58. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics, Sage.
59. How does training effect users’ attitudes and skills needed for highly automated driving?10.1016/j.trf.2019.09.001 ↗
60. Oviedo-Trespalacios, O., Tichon, J., and Briant, O. (2021). Is a flick-through enough? A content analysis of Advanced Driver Assistance Systems (ADAS) user manuals. PLoS ONE, 16.10.1371/journal.pone.0252688 ↗