Driving Intention Recognition and Lane Change Prediction on the Highway

TL;DR

This paper introduces a framework that uses external traffic data to recognize driver intentions and accurately predict lane change behaviors on highways, validated with real-world traffic data.

Contribution

It presents a novel online estimator for driver characteristics and a neural network-based predictor validated with naturalistic traffic data.

Findings

Effective identification of driving characteristics

Accurate lane change prediction in real-world scenarios

Validated framework with NGSIM data

Abstract

This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.