Accurate Trajectory Prediction for Autonomous Vehicles
This work addresses safe and comfortable driving for autonomous vehicles, but it appears incremental as it builds on existing neural network methods with specific enhancements.
The paper tackled the problem of predicting vehicle trajectories, angle, and speed for autonomous driving, achieving top performance by winning the top three places in the ICCV 2019 Learning to Drive challenge.
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.