Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

TL;DR

This paper investigates the use of efficient attention-based models with minimal map features for vehicle motion prediction, aiming to improve real-time performance and reduce reliance on extensive map data.

Contribution

It introduces a novel approach combining attention mechanisms with interpretable map features to achieve competitive results with less data and computational resources.

Findings

Achieves competitive performance on Argoverse 1.0 Benchmark

Uses minimal map information for efficient prediction

Demonstrates improved real-time applicability

Abstract

Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars. Current approaches tackle this problem using end-to-end networks, where the input data is usually a rendered top-view of the scene and the past trajectories of all the agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable Autonomous Driving (AD) system must produce reasonable predictions on time, however, despite many of these approaches use simple ConvNets and LSTMs, models might not be efficient enough for real-time applications when using both sources of information (map and trajectory history). Moreover, the performance of these models highly depends on the amount of training data, which can be expensive (particularly the annotated HD maps). In this work, we explore how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models, which take as input the past trajectories and map-based features from minimal map information to ensure efficient and reliable MP. These features represent interpretable information as the driveable area and plausible goal points, in opposition to black-box CNN-based methods for map processing.