Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data

TL;DR

This paper explores integrating synchronized human behavior data, such as eye-tracking and brainwaves, into autonomous driving systems to improve their performance and generalizability, using Carla simulation for evaluation.

Contribution

It introduces a novel method of collecting synchronized human and machine driving data to enhance autonomous driving through human attention guidance.

Findings

Human attention guidance improves driving performance.

Synchronization of human and machine data is feasible.

Human intention guidance remains challenging.

Abstract

This paper presents a pioneering exploration into the integration of fine-grained human supervision within the autonomous driving domain to enhance system performance. The current advances in End-to-End autonomous driving normally are data-driven and rely on given expert trials. However, this reliance limits the systems' generalizability and their ability to earn human trust. Addressing this gap, our research introduces a novel approach by synchronously collecting data from human and machine drivers under identical driving scenarios, focusing on eye-tracking and brainwave data to guide machine perception and decision-making processes. This paper utilizes the Carla simulation to evaluate the impact brought by human behavior guidance. Experimental results show that using human attention to guide machine attention could bring a significant improvement in driving performance. However, guidance by human intention still remains a challenge. This paper pioneers a promising direction and potential for utilizing human behavior guidance to enhance autonomous systems.