Accurate Trajectory Prediction for Autonomous Vehicles

TL;DR

This paper presents a neural network system for accurate vehicle trajectory prediction, integrating multiple inputs and leveraging pre-trained models to improve autonomous driving safety and performance.

Contribution

It introduces a novel neural network architecture that fuses various inputs and outputs, utilizing pre-trained models and output distribution modeling for enhanced accuracy.

Findings

Achieved top three positions in the ICCV 2019 Learning to Drive challenge.

Demonstrated improved trajectory, angle, and speed prediction accuracy.

Validated the effectiveness of input augmentation and output distribution modeling.

Abstract

Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving. We demonstrate the best predicted angle, speed, and best performance overall winning the top three places of the ICCV 2019 Learning to Drive challenge. Our key contributions are (i) a general neural network system architecture which embeds and fuses together multiple inputs by encoding, and decodes multiple outputs using neural networks, (ii) using pre-trained neural networks for augmenting the given input data with segmentation maps and semantic information, and (iii) leveraging the form and distribution of the expected output in the model.