MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction

TL;DR

MacFormer is a novel transformer-based framework that integrates map constraints and efficient modules to achieve real-time, robust trajectory prediction for autonomous vehicles, outperforming existing methods on multiple benchmarks.

Contribution

The paper introduces MacFormer, a transformer model with explicit map integration, multi-task optimization, and lightweight design, advancing real-time and robust trajectory prediction.

Findings

Achieved state-of-the-art performance on Argoverse 1, Argoverse 2, and nuScenes datasets.

Demonstrated lowest inference latency and smallest model size among competitors.

Proved robustness to imperfect tracklet inputs and versatility across classical models.

Abstract

Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains. Accurate prediction relies on comprehending the surrounding map, which significantly regularizes agent behaviors. However, existing methods have limitations in exploiting the map and exhibit a strong dependence on historical trajectories, which yield unsatisfactory prediction performance and robustness. Additionally, their heavy network architectures impede real-time applications. To tackle these problems, we propose Map-Agent Coupled Transformer (MacFormer) for real-time and robust trajectory prediction. Our framework explicitly incorporates map constraints into the network via two carefully designed modules named coupled map and reference extractor. A novel multi-task optimization strategy (MTOS) is presented to enhance learning of topology and rule constraints. We also devise bilateral query scheme in context fusion for a more efficient and lightweight network. We evaluated our approach on Argoverse 1, Argoverse 2, and nuScenes real-world benchmarks, where it all achieved state-of-the-art performance with the lowest inference latency and smallest model size. Experiments also demonstrate that our framework is resilient to imperfect tracklet inputs. Furthermore, we show that by combining with our proposed strategies, classical models outperform their baselines, further validating the versatility of our framework.