Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings for Automated Driving

TL;DR

This paper introduces CAWA, a context-aware advisory warning system for automated driving that adapts warnings based on driver activity to improve safety and awareness during non-driving tasks.

Contribution

The paper presents a novel context-aware warning system that dynamically adjusts warning modalities in automated driving, enhancing driver safety and engagement.

Findings

CAWA improves safer takeover behavior

CAWA enhances driver situational awareness

CAWA reduces attention demand

Abstract

In conditionally automated driving, drivers decoupled from driving while immersed in non-driving-related tasks (NDRTs) could potentially either miss the system-initiated takeover request (TOR) or a sudden TOR may startle them. To better prepare drivers for a safer takeover in an emergency, we propose novel context-aware advisory warnings (CAWA) for automated driving to gently inform drivers. This will help them stay vigilant while engaging in NDRTs. The key innovation is that CAWA adapts warning modalities according to the context of NDRTs. We conducted a user study to investigate the effectiveness of CAWA. The study results show that CAWA has statistically significant effects on safer takeover behavior, improved driver situational awareness, less attention demand, and more positive user feedback, compared with uniformly distributed speech-based warnings across all NDRTs.