Designing Smart and Autonomous Vehicles

Thu, 25 May 2023 00:00:00 +0000

Overview

Adoption of autonomous vehicles is held back less by the driving than by the passenger’s judgment of it. Trust has to be calibrated: too little and the technology goes unused, too much and it is relied on where it should not be. We study that calibration at the level of the individual, using virtual reality, biometric measurement, human-centered design, and data science, rather than treating drivers as a single population.

Trust is personal. A survey of 1,457 young adults, modeled with machine learning and SHAP, showed that perceptions of AV risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and mental models predict trust, while psychosocial traits and driving style contribute far less.

Explanations carry that trust, and they can fail. In a simulated driving study with 232 participants, errors in an AV’s explanations reduced comfort in relying on the vehicle, confidence in its ability, and explanation satisfaction, even though the driving itself was identical. Perceived harm and driving difficulty amplified the damage, so the contexts where an explanation matters most are where getting it wrong costs most.

Studying any of this requires observing the driver. DriveSimQuest is a VR driving simulator and research platform on the Meta Quest Pro, capturing gaze, facial expression, hand activity, and full-body gesture in real time, so that studying a driver’s affective state is a matter of designing the study rather than building the rig.

The same setting looks different when the driver is a person. Fleet drivers are involved in collisions that impose severe financial costs and endanger lives, and fleet companies rely on one-to-one coaching to prepare them. We characterize that coaching from both sides, surveying coaches and interviewing drivers, and find that manager-led coaching outperforms self-coaching across experiential outcomes.

Funding and External Collaborations

The autonomous vehicle trust work is a collaboration between the UCSD departments of Cognitive Science, Computer Science and Engineering, and external industry partners. The fleet driver coaching research is conducted with Lytx.

Improving Human-Autonomous Vehicle Interaction in Complex Systems

Wed, 01 Jan 2025 00:00:00 +0000

Predicting trust in autonomous vehicles: Modeling young adult psychosocial traits, risk-benefit attitudes, and driving factors with machine learning

Wed, 01 Jan 2025 00:00:00 +0000

What did my car say? impact of autonomous vehicle explanation errors and driving context on comfort, reliance, satisfaction, and driving confidence

Wed, 01 Jan 2025 00:00:00 +0000

Robert Kaufman | HXI - Human-centered eXtended Intelligence

Designing Smart and Autonomous Vehicles

Overview

Funding and External Collaborations

Improving Human-Autonomous Vehicle Interaction in Complex Systems

Predicting trust in autonomous vehicles: Modeling young adult psychosocial traits, risk-benefit attitudes, and driving factors with machine learning

What did my car say? impact of autonomous vehicle explanation errors and driving context on comfort, reliance, satisfaction, and driving confidence