Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow

TL;DR

This paper introduces a safety-first, human-like decision-making framework for autonomous vehicles that adapts to time-varying traffic, ensuring safety, comfort, and social compatibility through a hierarchical, attention-based, and reinforcement learning approach.

Contribution

The proposed SF-HLDM framework uniquely combines hierarchical structure, attention mechanisms, social compliance, and deep evolutionary reinforcement learning for improved autonomous driving decisions.

Findings

Enhanced safety margins in dynamic traffic scenarios

Improved decision flexibility and interpretability

Effective avoidance of local optima in decision-making

Abstract

Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills and usually do not make the same decisions even situate in the exactly same scenarios, leading to the data-driven methods suffer from poor migratability and high search cost problems, decreasing the efficiency and effectiveness of the behavior policy. In this research, we propose a safety-first human-like decision-making framework(SF-HLDM) for AVs to drive safely, comfortably, and social compatiblely in effiency. The framework integrates a hierarchical progressive framework, which combines a spatial-temporal attention (S-TA) mechanism for other road users' intention inference, a social compliance estimation module for behavior regulation, and a Deep Evolutionary Reinforcement Learning(DERL) model for expanding the search space efficiently and effectively to make avoidance of falling into the local optimal trap and reduce the risk of overfitting, thus make human-like decisions with interpretability and flexibility. The SF-HLDM framework enables autonomous driving AI agents dynamically adjusts decision parameters to maintain safety margins and adhering to contextually appropriate driving behaviors at the same time.