Crowdsourcing Autonomous Traffic Simulation

TL;DR

The paper introduces CATS, a novel framework that combines economic theories and emotion-based modeling to enhance autonomous traffic simulation, aiming to improve safety and traffic flow efficiency.

Contribution

It presents a unique integration of economic coupling constraints and emotion-driven behavior modeling within traffic simulation, advancing autonomous traffic management.

Findings

Significantly reduces traffic accidents

Improves urban traffic conditions

Enhances safety in autonomous traffic systems

Abstract

We present an innovative framework, Crowdsourcing Autonomous Traffic Simulation (CATS) framework, in order to safely implement and realize orderly traffic flows. We firstly provide a semantic description of the CATS framework using theories of economics to construct coupling constraints among drivers, in which drivers monitor each other by making use of transportation resources and driving credit. We then introduce an emotion-based traffic simulation, which utilizes the Weber-Fechner law to integrate economic factors into drivers' behaviors. Simulation results show that the CATS framework can significantly reduce traffic accidents and improve urban traffic conditions.