Authors: Lu Zhang, Peiliang Li, Sikang Liu, Shaojie Shen

Published on: February 04, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.02519

Summary

• What is new: SIMPL integrates a global feature fusion module and uses Bernstein basis polynomials for trajectory decoding, enhancing both the accuracy and speed of motion prediction for autonomous vehicles.
• Why this is important: Existing motion prediction methods for autonomous vehicles either have high accuracy but are computationally intensive, or compromise accuracy for generalizability.
• What the research proposes: SIMPL utilizes a compact global feature fusion module for efficient motion prediction and introduces continuous trajectory parameterization with Bernstein basis polynomials for better planning.
• Results: SIMPL demonstrates highly competitive performance on Argoverse 1 & 2 benchmarks, combining improved accuracy with lower inference latency suitable for real-world applications.

Technical Details

Technological frameworks used: nan

Models used: Global feature fusion module, Bernstein basis polynomials for trajectory decoding.

Data used: Argoverse 1 & 2 motion forecasting benchmarks.

Potential Impact

The autonomous vehicle industry, including manufacturers and autonomous driving software companies, could significantly benefit from the insights and methodologies presented in the paper.

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