# Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1807.09995 · HTML: https://mlsanity.com/paper/1807.09995/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 0 of its 34 references that are in the map.
- First posted on arXiv 26 Jul 2018.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)

## Built on by (most cited first, 29 of 29)

- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Spectral Temporal Graph Neural Network for Trajectory Prediction](https://mlsanity.com/paper/2106.02930/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning](https://mlsanity.com/paper/2106.13364/) (2021)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [Causal-based Time Series Domain Generalization for Vehicle Intention Prediction](https://mlsanity.com/paper/2112.02093/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023)
- [Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques](https://mlsanity.com/paper/2109.10656/) (2021)
- [Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks](https://mlsanity.com/paper/2303.16109/) (2023)
- [KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections](https://mlsanity.com/paper/2404.11181/) (2024)
- [Maneuver-Aware Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2104.14079/) (2021)
- [Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis](https://mlsanity.com/paper/2403.09571/) (2024)
- [INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering](https://mlsanity.com/paper/2503.04952/) (2025)
- [GAMDTP: Dynamic Trajectory Prediction with Graph Attention Mamba Network](https://mlsanity.com/paper/2504.04862/) (2025)
- [DyTTP: Trajectory Prediction with Normalization-Free Transformers](https://mlsanity.com/paper/2504.05356/) (2025)
- [Open-set Intersection Intention Prediction for Autonomous Driving](https://mlsanity.com/paper/2103.00140/) (2021)
- [Maneuver-based Anchor Trajectory Hypotheses at Roundabouts](https://mlsanity.com/paper/2104.11180/) (2021)
- [Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field](https://mlsanity.com/paper/2211.04890/) (2022)
- [Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability](https://mlsanity.com/paper/2312.12123/) (2023)
- [Critical Example Mining for Vehicle Trajectory Prediction using Flow-based Generative Models](https://mlsanity.com/paper/2410.16083/) (2024)
- [Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction](https://mlsanity.com/paper/2508.17797/) (2025)
- [An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems](https://mlsanity.com/paper/2308.01369/) (2023)
- [Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network](https://mlsanity.com/paper/2409.18399/) (2024)
- [EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving](https://mlsanity.com/paper/2502.19260/) (2025)
- [Wireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity](https://mlsanity.com/paper/2511.18368/) (2025)

## Abstract (arXiv)

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.