Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting

This paper presents a novel vehicle motion forecasting method based on multi-head attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-modal probability density functions of their positions. Its architecture uses multi-head attention to account for complete interactions between all vehicles, and long short-term memory layers for encoding and forecasting. It relies solely on vehicle position tracks, does not need maneuver definitions, and does not represent the scene with a spatial grid. This allows it to be more versatile than similar model while combining any forecasting capabilities, namely joint forecast with interactions, uncertainty estimation, and multi-modality. The resulting prediction likelihood outperforms state-of-the-art models on the same dataset.

Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset

In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those baseline models are a constant velocity model and a single-vehicle prediction model. They are applied on the NGSIM US-101 and I-80 datasets using only relative positions. Thus, the process can be reproduced with any database containing tracking of vehicle positions. The evaluation reports Root Mean Squared Error (RMSE), Final Displacement Error (FDE), Negative Log-Likelihood (NLL), and Miss Rate (MR). The NLL estimation needs a careful definition because several formulations that differ from the mathematical definition are used in other works. This article is meant to be used along with the published code to establish baselines for further work. An extension is proposed to replace the constant velocity assumption with a learned model using a recurrent neural network. This brings good improvements in accuracy and uncertainty estimation and opens possibilities for both complex and interpretable models.