New MotoVerse benchmark targets predictive perception for two-wheelers

Researchers have introduced MotoVerse, a new benchmark designed to improve predictive perception for motorized two-wheelers (MTWs) in complex traffic environments, particularly in the Global South. The benchmark includes a dataset of nearly 1,000 maneuver clips and trajectory data for over 31,000 road agents, enabling tasks like maneuver anticipation and trajectory prediction. Initial experiments show that while video backbones achieve high accuracy early in a maneuver, general-purpose vision-language models require more observation time and lag behind conventional models in performance, though fine-tuning shows promise. AI

IMPACT This benchmark could lead to improved AI systems for autonomous driving and ADAS, enhancing safety for two-wheeler riders in complex traffic scenarios.

RANK_REASON The cluster contains a research paper introducing a new benchmark dataset and evaluation methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• ERMA
• Global South
• Hugging Face
• LoRA+
• Motorized Two-Wheelers
• MotoVerse
• Vision-Language Models

• paper
• product

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