Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

By Alex Zyner, Stewart Worrall, Eduardo Mario Nebot. 2018

205 citations recorded by the source corpus.

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 epicenter 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 probability. 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.

Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Imitative Models for Flexible Inference, Planning, and Control (2018, background): “Typically this involves recurrent neural networks combined with Gaussian density layers or generative models based on some context inputs such as LIDAR, images, or known positions of external agents (Lee et al., 2017; Schmerling et al., 2018; Zyner et al., 2018; Gupta et al., 2018; Ma et al., 2017).”
Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review (2020, methodology): “isadvantages Works Summary Track History of the TV - Complies with limited observability of the EV - Does not consider the impact of environment and interaction among vehicles on TV’s behaviour [14], [16], [15], [17], [18] Track history of the TV’s states (e.g. position, velocity, heading, and etc.)”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “[74] adopt the weighted Gaussian Mixture Model (GMM) for prediction, and its parameters are obtained by an encoderdecoder three-layer LSTM, and then predicted trajectories are clustered using the modal with the highest probability.”