Motion Forecasting Benchmark

Motion forecasting benchmarks evaluate algorithms that predict the future movements of multiple agents (e.g., vehicles, pedestrians) in dynamic environments, crucial for autonomous driving and robotics. Current research emphasizes improving prediction accuracy and efficiency through various model architectures, including transformers, autoregressive models, and graph neural networks, often incorporating techniques like self-supervised pre-training and efficient feature fusion. These benchmarks drive advancements in multi-agent trajectory prediction, impacting the safety and reliability of autonomous systems by enabling more accurate and robust decision-making in complex scenarios. The development of efficient and accurate models is a key focus, with a strong emphasis on real-time performance for practical deployment.