Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review

TL;DR

This review paper discusses the importance of realistic and adversarial traffic entities in simulation environments to better validate autonomous vehicle planning algorithms, highlighting current approaches, datasets, and future challenges.

Contribution

It categorizes existing traffic simulation approaches based on entity and scenario behaviors, and analyzes datasets and open challenges in the field.

Findings

Existing simulations often lack reactive and adversarial traffic entities.

Categorization of traffic entity behaviors and scenario control methods.

Identification of open challenges for future research in traffic simulation.

Abstract

Despite advancements in perception and planning for autonomous vehicles (AVs), validating their performance remains a significant challenge. The deployment of planning algorithms in real-world environments is often ineffective due to discrepancies between simulations and real traffic conditions. Evaluating AVs planning algorithms in simulation typically involves replaying driving logs from recorded real-world traffic. However, entities replayed from offline data are not reactive, lack the ability to respond to arbitrary AV behavior, and cannot behave in an adversarial manner to test certain properties of the driving policy. Therefore, simulation with realistic and potentially adversarial entities represents a critical task for AV planning software validation. In this work, we aim to review current research efforts in the field of traffic simulation, focusing on the application of advanced techniques for modeling realistic and adversarial behaviors of traffic entities. The objective of this work is to categorize existing approaches based on the proposed classes of traffic entity behavior and scenario behavior control. Moreover, we collect traffic datasets and examine existing traffic simulations with respect to their employed default traffic entities. Finally, we identify challenges and open questions that hold potential for future research.