Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference

TL;DR

This work demonstrates an approach for personalizing driver interaction via driver safety interfaces that are are triggered based on the inference of the driver’s latent cognitive states from their driving behavior and reveals that this approach is more effective in influencing driver behavior in yellow light zones by reducing their inclination to run through them.

Abstract

Recent advances in AI and intelligent vehicle technology hold promise to revolutionize mobility and transportation, in the form of advanced driving assistance (ADAS) interfaces. Although it is widely recognized that certain cognitive factors, such as impulsivity and inhibitory control, are related to risky driving behavior, play a significant role in on-road risk-taking, existing systems fail to leverage such factors. Varying levels of these cognitive factors could influence the effectiveness and acceptance of driver safety interfaces. We demonstrate an approach for personalizing driver interaction via driver safety interfaces that are triggered based on a learned recurrent neural network. The network is trained from a population of human drivers to infer impulsivity and inhibitory control from recent driving behavior. Using a high-fidelity vehicle motion simulator, we demonstrate the ability to deduce these factors from driver behavior. We then use these inferred factors to make instantaneous determinations on whether or not to engage a driver safety interface. This interface aims to decrease a driver's speed during yellow lights and reduce their inclination to run through them.

Figures (7)

• Figure 1: A conceptual overview of our framework. Latent factors embed cognitive measures from the driving behavior, and used to inform HMI choice(dashed lines). Solid line marked the observable driving behavior and personalized HMI.
• Figure 2: Overall system architecture, including context encoder, decoder for future state and action prediction, outputs of cognitive measures, and latent factors used for human-machine interface selection and decision-making.
• Figure 3: Example of HMI types used in the experiment when nearing a traffic light. a) transverse markings. b) yellow circle. c) the effect of different choices of HMI types on the mean speed at yellow traffic lights.
• Figure 4: An illustration of the driving motion simulator used for data collection.
• Figure 5: Interaction plots showing how the presence of the HMI interacted with different factors. From top left to bottom right: a) BAS Fun Seeking: Motivation to find novel rewards spontaneously; b) SSRT: Stop Signal Reaction Time; c) UPPS-P - Positive Urgency: Tendency to act impulsively due to positive affect; d) DBQ Ordinary Violations: Self-reported ordinary driving violations.