SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving (2402.02519v1)

Abstract: This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but repetitive computations and scene-centric methods with compromised accuracy and generalizability, SIMPL delivers real-time, accurate motion predictions for all relevant traffic participants. To achieve improvements in both accuracy and inference speed, we propose a compact and efficient global feature fusion module that performs directed message passing in a symmetric manner, enabling the network to forecast future motion for all road users in a single feed-forward pass and mitigating accuracy loss caused by viewpoint shifting. Additionally, we investigate the continuous trajectory parameterization using Bernstein basis polynomials in trajectory decoding, allowing evaluations of states and their higher-order derivatives at any desired time point, which is valuable for downstream planning tasks. As a strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 & 2 motion forecasting benchmarks compared with other state-of-the-art methods. Furthermore, its lightweight design and low inference latency make SIMPL highly extensible and promising for real-world onboard deployment. We open-source the code at https://github.com/HKUST-Aerial-Robotics/SIMPL.

• The paper introduces SIMPL, a novel multi-agent prediction baseline that synergizes scene-centric and agent-centric methods via a symmetric fusion Transformer.
• It employs Bernstein polynomial-based trajectory parameterization to ensure smooth, physically consistent predictions with stable numerical control.
• Empirical results on Argoverse datasets demonstrate SIMPL’s competitive accuracy and reduced computational footprint compared to state-of-the-art models.

An Expert Analysis of SIMPL: A Methodology for Efficient Multi-Agent Motion Prediction in Autonomous Driving

The paper "SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving" presents a novel framework for motion prediction in autonomous systems. The research provides significant contributions by addressing the challenges associated with conventional agent-centric and scene-centric methodologies.

Context Representation and Scene Modeling

Within autonomous driving systems, the accuracy of trajectory predictions for surrounding traffic participants is crucial for making informed decisions regarding vehicle navigation and safety. Traditional approaches have centered around two predominant methods for scene representation: rasterized and vectorized inputs. While rasterized methods tend to suffer from information loss due to limited receptive fields, vectorized methods encapsulate features through geometrically coherent annotations using point sets or polylines. This paper innovatively combines these methodologies by proposing an instance-centric model that enables more robust and accurate predictions.

SIMPL distinguishes itself by employing a symmetric fusion Transformer (SFT), allowing for efficient global feature aggregation while maintaining viewpoint-invariance. This is operationalized through a directed message-passing mechanism, which contrasts with methods such as HiVT, HDGT, and GoRela, offering a computationally lighter alternative without compromising prediction accuracy.

Theoretical Framework and Technical Approach

The paper introduces Bernstein basis polynomials as a means of trajectory parameterization, leveraging their ability to ensure smooth and efficient computation of trajectories and their derivatives. This results in trajectories that maintain physical consistency, aligning well with the requirements of downstream planning modules. Unlike monomial basis polynomials, Bernstein polynomials offer numerically stable control points with well-defined spatial interpretation, enhancing both prediction accuracy and ease of integration within real-time autonomous systems.

SIMPL's architecture embraces a straightforward, lightweight design, making it highly adaptable and efficient for onboard deployment. Despite being simplistic in its structuring, SIMPL demonstrates competitive results on benchmark datasets like Argoverse 1 and 2. The model achieves this through an efficient implementation of symmetric scene modeling, reinforcing its applicability for real-world scenarios where computational resources are limited.

Empirical Findings

The empirical evaluation showcases SIMPL's effectiveness, outperforming several state-of-the-art models with a significantly reduced computational footprint. In particular, the proposed methodology achieves notable results in minimum average displacement error (minADE$_k$) and minimum final displacement error (minFDE$_k$), metrics pivotal in evaluating prediction reliability in diverse driving scenarios. The paper's ablative analysis substantiates the model's robustness and highlights the importance of trajectory parameterization and the auxiliary yaw angle loss in augmenting prediction accuracy.

Implications and Future Perspectives

The implications of SIMPL are profound, as it provides a scalable, efficient baseline for multi-agent motion prediction. By balancing computational efficiency with predictive precision, SIMPL holds promise as a foundation for enhancing adaptability and responsiveness in autonomous systems. The introduction of continuous trajectory representation through Bernstein polynomials also sets the stage for further exploration into more refined prediction models that can seamlessly integrate into broader autonomous system architectures.

Going forward, future research should explore the integration of advanced self-supervised learning techniques, as indicated in recent works like SSL-Lanes and Forecast-MAE, to further bolster the model’s capacity to anticipate complex driving scenes. The prospect of extending SIMPL’s capabilities through iterative refinement and incorporating joint transformations for comprehensive multi-agent predictions is an exciting avenue for future inquiry.

In summary, the paper effectively contributes to the domain by refining the motion prediction process in autonomous systems, offering both theoretical insights and practical advancements that align with the ongoing evolution of intelligent transportation systems.