Driving Intention Recognition and Lane Change Prediction on the Highway
This work addresses safety and automation challenges for autonomous vehicles and drivers, but it is incremental as it builds on existing methods for behavior prediction.
The paper tackled the problem of recognizing driving intentions and predicting lane changes on highways using external traffic data, achieving effective identification and accurate behavior prediction validated with real-world NGSIM data.
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.