GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting

2022/11/04 by Alexander Cui, Sergio Casas, Cui, Alexander +9 · 15 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Robotics (cs.RO) #Time Series Analysis and Forecasting #Traffic Prediction and Management Techniques #cs.AI #cs.CV #cs.LG #cs.MA #cs.RO

paper · pdf · doi:10.48550/arxiv.2211.02545

openalex publication_date 2022/11/04 · arxiv created 2022/11/08 · arxiv updated 2022/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The task of motion forecasting is critical for self-driving vehicles (SDVs) to be able to plan a safe maneuver. Towards this goal, modern approaches reason about the map, the agents' past trajectories and their interactions in order to produce accurate forecasts. The predominant approach has been to encode the map and other agents in the reference frame of each target agent. However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent. To tackle the scaling challenge, the solution thus far has been to encode all agents and the map in a shared coordinate frame (e.g., the SDV frame). However, this is sample inefficient and vulnerable to domain shift (e.g., when the SDV visits uncommon states). In contrast, in this paper, we propose an efficient shared encoding for all agents and the map without sacrificing accuracy or generalization. Towards this goal, we leverage pair-wise relative positional encodings to represent geometric relationships between the agents and the map elements in a heterogeneous spatial graph. This parameterization allows us to be invariant to scene viewpoint, and save online computation by re-using map embeddings computed offline. Our decoder is also viewpoint agnostic, predicting agent goals on the lane graph to enable diverse and context-aware multimodal prediction. We demonstrate the effectiveness of our approach on the urban Argoverse 2 benchmark as well as a novel highway dataset.

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• Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
• Goal-based Trajectory Prediction for improved Cross-Dataset Generalization
• Linear Memory SE(2) Invariant Attention
• LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction
• When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
• Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset
• DONUT: A Decoder-Only Model for Trajectory Prediction
• RetroMotion: Retrocausal Motion Forecasting Models are Instructable
• HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning
• Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions
• Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning
• Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance
• Dynamic Intent Queries for Motion Transformer-based Trajectory Prediction
• A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition

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