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The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
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The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
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In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.
Forward citations
Cited by 7 Pith papers
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Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
Bench2Drive-Robust is a new closed-loop benchmark that evaluates end-to-end autonomous driving models under deployment perturbations from camera failures, ego-state errors, and compute delays, showing substantial perf...
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Belief-Space Perception Routing under Coupled Sensor Faults and Compute Contention
A belief-space router coupling sensor-fault and compute-contention estimates cuts deadline misses under imposed coupling but finds no natural coupling in real adverse-weather data.
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ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness
Real adverse-weather camera–LiDAR–radar MCQs expose VLM failures from observability estimation through spatial grounding to trajectory safety, partially mitigated by SFT+RL.
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Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
A new framework factorizes weather video synthesis into semantic appearance anchoring, physics-informed Gaussian particle simulation under gravity/wind/turbulence, and geometry-grounded alignment to produce diverse re...
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Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
OneVL is the first latent CoT method to exceed explicit CoT accuracy on four driving benchmarks while running at answer-only speed, by supervising latent tokens with a visual world model decoder.
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Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
OneVL achieves superior accuracy to explicit chain-of-thought reasoning at answer-only latency by supervising latent tokens with a visual world model decoder that predicts future frames.
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WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
A five-aspect, 24-metric benchmark, a 26K human-annotated dataset, and an AI evaluator show that today's driving world models cannot simultaneously look real, respect geometry, and behave safely.
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