# Driving Intention Recognition and Lane Change Prediction on the Highway

arXiv: https://arxiv.org/abs/1908.10820 · HTML: https://mlsanity.com/paper/1908.10820/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2501.14992/) (2025)
- [Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2506.11925/) (2025)
- [Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation](https://mlsanity.com/paper/2210.07246/) (2022)
- [Vehicle Behavior Prediction by Episodic-Memory Implanted NDT](https://mlsanity.com/paper/2402.08423/) (2024)
- [Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving](https://mlsanity.com/paper/2509.04712/) (2025)
- [Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data](https://mlsanity.com/paper/2601.22242/) (2026)

## Abstract (arXiv)

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.