A Novel Method for Lane-change Maneuver in Urban Driving Using Predictive Markov Decision Process

TL;DR

This paper introduces a predictive Markov decision process for urban lane-change maneuvers that accounts for uncertainties in surrounding vehicle behaviors, aiming to improve safety and efficiency.

Contribution

It develops a hidden Markov model to capture uncertainties and integrates it into a Markov decision process for safer lane changes in urban environments.

Findings

Effective modeling of vehicle behavior uncertainties.

Improved lane-change decision-making in simulations.

Reduced crash probabilities in tested scenarios.

Abstract

Lane-change maneuver has always been a challenging task for both manual and autonomous driving, especially in an urban setting. In particular, the uncertainty in predicting the behavior of other vehicles on the road leads to indecisive actions while changing lanes, which, might result in traffic congestion and cause safety concerns. This paper analyzes the factors related to uncertainty such as speed range change and lane change so as to design a predictive Markov decision process for lane-change maneuver in the urban setting. A hidden Markov model is developed for modeling uncertainties of surrounding vehicles. The reward model uses the crash probabilities and the feasibility/distance to the goal as primary parameters. Numerical simulation and analysis of two traffic scenarios are completed to demonstrate the effectiveness of the proposed approach.