Advanced Vehicle Technology (AVT) Consortium

The Advanced Vehicle Technology (AVT) Consortium was launched in September 2015 with the goal of achieving a data-driven understanding of how drivers engage with and leverage vehicle automation, driver assistance technologies, and the range of in-vehicle and portable technologies for connectivity and infotainment appearing in modern vehicles.

Using advanced computer-vision software and big data analytics, researchers are gathering data to quantify drivers' actions, such as how they respond to various driving situations and perform other actions like eating or having conversations behind the wheel. The research is studying the moments when control transfers from the driver to the car and back again, as well as how drivers respond to alarms (lane keeping, forward collision, proximity detectors, etc.) and interact with assistive and safety technologies (e.g., adaptive cruise control, semi-autonomous parking assistance, vehicle infotainment and communications systems, smartphones and more). The effort aims to develop human-centric insights that drive the safety efficacy of automated vehicle technology development and advances the consumer's understanding of appropriate technology usage.

Who is Involved?

As an academic industry partnership, the AVT Consortium brings together key stakeholders in the automotive ecosystem in a unique collaborative effort at the Massachusetts Institute of Technology (MIT).

The founding members of the AVT Consortium are the MIT AgeLab, Touchstone Evaluations, and Agero.

Consortium membership also includes automakers, insurance companies, tier-1 suppliers, and research organizations:

  • Agero
  • Allstate
  • Aptiv
  • Arriver
  • Audi
  • Autoliv
  • Bosch
  • CARIAD*
  • Consumer Reports
  • Google
  • Honda
  • Insurance Institute for Highway Safety
  • Jaguar Land Rover
  • JD Power
  • Lear
  • Liberty Mutual
  • Nissan
  • Polestar*
  • Progressive
  • Seeing Machines
  • Smart Eye
  • Subaru
  • The LAB (GIE Stellantis & Groupe Renault)
  • Toyota
  • TravelCenters of America
  • Travelers
  • Veoneer
  • Volvo Car Corporation*
  • Zenseact

*Member Affiliate

Data Collection

Data collection began in January 2016 and to date has focused on Tesla models S, X and 3 vehicles, Cadillac CT6 vehicles equipped with Super Cruise, Volvo S90 vehicles equipped with Pilot Assist technology, and Range Rover Evoque vehicles with a range of advanced driver assistance features. The effort adds new vehicles of interest as they are introduced into the marketplace, with the most recent being the Ford Mustang Mach-E with Blue Cruise. Recorded data streams include IMU, GPS, CAN messages, and high-definition color video of the driver's face, body positioning, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). Questionnaire and interview data is collected to provide qualitative insight in additional to the vast amount of objective data obtained.

In addition to analytic efforts being carried out collaboratively within the consortium, individual members are utilizing portions of the dataset to delve deeper into questions of specific interest to their organizations, both with and without direct involvement of the MIT AgeLab. At the most fundamental level, these efforts aim to identify how technology and other factors related to automation adoption and use can be improved in ways that save lives.

Publications

  1. Domeyer, J.E., Lee, J.D., Toyoda, H., Mehler, B. & Reimer, B. (2022). Interdependence in Vehicle-Pedestrian Encounters and its Implications for Vehicle Automation. IEEE Transactions on Intelligent Transportation Systems. 23(5). 4122-4134.
  2. Haus, S.H., Gershon, P., Mehler, B., & Reimer, B. (2022). Characterizing Driver Speeding Behavior when Using Partial-Automation in Real-World Driving. Traffic Injury Prevention (Accepted).
  3. Haus, S.H., Gershon, P., Mehler, B., & Reimer, B. (2022). Speeding behavior when using automation: a descriptive analysis of naturalistic driving data (LECT346s1). Accepted for presentation at the 66th Human Factors & Ergonomics Society International Annual Meeting.
  4. Hu, W., Cicchino, J.B., Reagan, I.J., Monfort, S.S., Gershon, P., Mehler, B. & Reimer, B. (2022). Use of Level 1 and 2 driving automation on horizontal curves on interstates and freeways. Transportation Research Part F: Psychology and Behaviour. 89. 64-71.
  5. Monfort, S.S., Reagan, I.J., Cicchino, J.B., Hu, W., Gershon, P., Mehler, B. & Reimer, B. (2022). Speeding behavior while using adaptive cruise control and lane centering in free flow traffic. Traffic Injury Prevention. 23(2). 85-90.
  6. Noonan, T.Z., Gershon, P., Mehler, B., & Reimer, B. (2022). Characterizing the use of Tesla’s auto lane change feature in driver-initiated maneuvers. (LECT394s1). Accepted for presentation at the 66th Human Factors & Ergonomics Society International Annual Meeting.
  7. Noonan, T.Z., Gershon, P., Mehler, B., & Reimer, B. (2022). Interdependence of driver and pedestrian behavior in naturalistic roadway negotiations. Traffic Injury Prevention (Accepted).
  8. Payyanadan, R.P. & Angell, L.S. (2022). A Framework for Building Comprehensive Driver Profiles. Information. 13(61).
  9. Reagan, I.J., Cicchini, J.B., Teoh, E.R., Reimer, B., Mehler, B., & Gershon, P. (2022). Behavior change over time when driving with adaptive cruise control. Accepted for presentation at the 66th Human Factors & Ergonomics Society International Annual Meeting.
  10. Seaman, S., Gershon, P., Angell, L., Mehler, B. & Reimer, B. (2022). Non-Driving-Related Task Engagement: The Role of Speed. Safety. 8(34).
  11. Seaman, S., Gershon, P., Angell, L., Mehler, B. & Reimer, B. (2022). Evaluating the Associations between Forward Collision Warning Severity and Driving Context. Safety. 8(5).
  12. Yang, S., Lenné, M.G., Reimer, B., Gershon, P. (2022). Modeling Driver-Automation Interaction using Naturalistic Multimodal Driving Dataset. Accepted for presentation at the 66th Human Factors & Ergonomics Society International Annual Meeting.
  13. Banerjee, S., Joshi, A., Turcot, J., Reimer, B. & Mishra, T. (2021). Driver glance classification in-the-wild: towards generalization across domains and subjects. 2021 IEEE International Conference on Automatic Fae and Gesture Recognition (FG 2021).
  14. Gershon, P., Seaman, S., Mehler, B., Reimer, B. & Coughlin, J. (2021). Driver behavior and the use of automation in real-world driving. Accident Analysis and Prevention. 158, 106319.
  15. Guerin, F., Gershon, P., Mehler, B. & Reimer, B. (2021). Combining visualization and machine learning methods to infer driver behavior in low-speed parking maneuvers. Driving Assessment Student Session.
  16. Landry, S., Seppelt, B., Krampell, M. & Russo, L. (2021). Exploring User Understanding of ADAS Iconography Using Novel Survey Methods. Transportation Research Record. Also appeared in (2021) Proceedings of The Transportation Research Board 100th Annual Meeting, Washington, DC.
  17. Lee, C., Gershon, P., Reimer, B., Mehler, B., & Coughlin, J.F. (2021). Consumer knowledge and acceptance of driving automation: changes over time and across age groups. Proceedings of the 65th Annual Meeting of the Human Factors and Ergonomics Society.
  18. Morando, A., Gershon, P., Mehler, B., & Reimer, B. (2021). A model for naturalistic glance behavior around Tesla Autopilot disengagements. Accident Analysis and Prevention. 161, 106348.
  19. Morando, A., Gershon, P., Mehler, B., & Reimer, B. (2021). Visual attention and steering wheel control: From engagement to disengagement of Tesla Autopilot. Proceedings of the 65th Annual Meeting of the Human Factors and Ergonomics Society.
  20. Reagan, I.J., Teoh, E.R., Cicchino, J.B., Gershon, P., Reimer, B., Mehler, B. & Seppelt, B. (2021). Disengagement from driving when using automation during a 4-week field trial. Transportation Research Part F: Psychology and Behaviour. 82. 400–411.
  21. Ding, L., Glazer, M., Wang, M., Mehler, B., Reimer, B. ;& Fridman, L. (2020). MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios. The 2020 IEEE Intelligent Vehicles Symposium (IV). Las Vegas, NV. pp. 232-237. DOI: 10.1109/IV47402.2020.9304677.
  22. Morando, A., Gershon, P., Mehler, B. & Reimer, B. (2020). Driver-initiated Tesla Autopilot Disengagements in Naturalistic Driving. Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicle Applications (AutomotiveUI '20), Virtual Event. pp. 57-65. DOI: 10.1145/3409120.3410644.
  23. Biever, W., Angell, L., & Seaman, S. (2020). Automated driving system collisions: early lessons. Human factors, 62(2), 249-259.
  24. Ding, L., Glazer, M., Terwilliger, J., Reimer, B. & Fridman, L. (2020). MIT DriveSeg (Semi-auto) Dataset: Large-scale Semi-automated Annotation of Semantic Driving Scenes. Massachusetts Institute of Technology AgeLab Technical Report 2020-2, Cambridge, MA.
  25. Landry, S., Seppelt, B., Russo, L., Mehler, B., Angell, L., Gershon, P., Reimer, B. (2020). Perceptions of Two Unique Lane Centering Systems: An FOT Interview Analysis. SAE Technical Paper 2020-01-0108.
  26. Angell, L. Seaman, S., Payyanadan, R., Biever, W., Seppelt, B., Mehler, B., Reimer, B. (2019). In the Context of Whole Trips: New Insights Into Driver Management of Attention and Tasks. Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. Santa Fe, NM. pp. 1-7.
  27. Fridman, L., Brown, D.E., Glazer, M., Angell, W., Dodd, S., Jenik, B., Terwilliger, J., Patsekink, A., Kindelsberger, J., Ding, L., Seaman, S., Abraham, H., Mehler, A., Sipperley, A., Pettinato, A., Seppelt, B.D., Angell, L., Mehler, B. & Reimer, B. (2019). MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation. IEEE Access, 7(1), pp. 102021-102038.
  28. Fridman, L., Ding, L., Jenik, B. & Reimer, B. (2019). Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR): Workshop on Autonomous Driving.
  29. Lee, C., Seppelt, B., Abraham, H., Reimer, B., Mehler, B., Coughlin, J.F. (2019). Consumer Comfort with Vehicle Automation: Changes Over Time. Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. Santa Fe, NM. pp. 412-418.
  30. Lee, C., Seppelt, B., Reimer, B., Mehler, B., Coughlin, J.F. (2019). Acceptance of Vehicle Automation: Effects of Demographic Traits, Technology Experience and Media Exposure. Proceedings of the 63rd Annual Meeting of the Human Factors and Ergonomics Society. Seattle, WA. pp. 2066-2070.
  31. Reagan, I.J., Hu., W., Cicchino, J., Seppelt, B., Fridman, L., Glazer, M. (2019). Measuring Adult Drivers' Use of Level 1 and 2 Driving Automation by Roadway Function Class. Proceedings of the 63rd Annual Meeting of the Human Factors and Ergonomics Society. Seattle, WA. pp. 2093-2097.
  32. Robertson, J., Kothakonda, A., Cheng, K., Wong, J., Landry, S., Mehler, A., Mehler, B., Reimer, B. (2019). Comparing Training Methods for Using a Semi-Automated Parking System. Proceedings of The Transportation Research Board 98th Annual Meeting, Washington, DC.
  33. Seppelt, B., Reimer, B., Russo, L., Mehler, B., Fisher, J., Friedman, D. (2019). Consumer Confusion with Levels of Vehicle Automation. Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. Santa Fe, NM. pp. 391-397.
  34. Terwilliger, J., Glazer, M., Schmidt, H., Domeyer, J., Toyoda, H., Mehler, B., Reimer, B. & Fridman, L. (2019). Dynamics of Pedestrian Crossing Decisions Based on Vehicle Trajectories in Large-Scale Simulated and Real-World Data. Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design. Santa Fe, NM. pp. 64-70.
  35. Abraham, H., Mehler, B., Reimer, B. (2018). Learning to Use In-Vehicle Technologies: Consumer Preferences and Effects on Understanding. Proceedings of the 62nd Annual Meeting of the Human Factors and Ergonomics Society. Philadelphia, PA. pp. 1589-1593.
  36. Abraham, H., Reimer, B., Seppelt, B., Fitzgerald, C., Mehler, B., Coughlin, J.F. (2018). Consumer Interest in Automation: Change over One Year. Proceedings of The Transportation Research Board 97th Annual Meeting, Washington, DC.
  37. Abraham, H., Lee, C., Brady, S., Fitzgerald, C., Mehler, B., Reimer, B. & Coughlin, J.F. (2017). Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. Proceedings of The Transportation Research Board 96th Annual Meeting, Washington, D.C.
  38. Abraham, H., McAnulty, H., Mehler, B., & Reimer, B. (2017). Case study of today's automotive dealerships: introduction and delivery of advanced driver assistance systems. Transportation Research Record, No. 2660, pp. 7-14.
  39. Seppelt, B., Reimer, B., Angell, L., Seaman, S. (2017). Considering the human across levels of automation: implications for reliance. Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. Manchester Village, VT.
  40. Abraham, H., Reimer, B., & Mehler, B. (2017). Advanced driver assistance systems (ADAS): a consideration of driver perceptions on training, usage & implementation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), pp. 1954-1958. Austin, TX.
  41. Abraham, H., Seppelt, B., Mehler, B., & Reimer, B. (2017). What's in a name: vehicle technology branding & consumer expectations for automation. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI), Oldenburg, Germany, September 24-27.

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