✨DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning

📝 Summary:
DrivePI is a new spatial-aware 4D MLLM for autonomous driving, unifying understanding, 3D perception, prediction, and planning. It integrates point clouds, images, and language instructions, achieving state-of-the-art performance by outperforming existing VLA and specialized VA models.

🔹 Publication Date: Published on Dec 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12799
• PDF: https://arxiv.org/pdf/2512.12799
• Github: https://github.com/happinesslz/DrivePI

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#AutonomousDriving #MLLM #ComputerVision #DeepLearning #AI

✨Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

📝 Summary:
Vision-Language-Action VLA models integrate visual, linguistic, and action capabilities for autonomous driving. They aim for interpretable and human-aligned policies, addressing prior system limitations. This paper characterizes VLA paradigms, datasets, and future challenges.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16760
• PDF: https://arxiv.org/pdf/2512.16760
• Project Page: https://worldbench.github.io/vla4ad
• Github: https://github.com/worldbench/awesome-vla-for-ad

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#VLAModels #AutonomousDriving #AI #DeepLearning #Robotics

✨RadarGen: Automotive Radar Point Cloud Generation from Cameras

📝 Summary:
RadarGen synthesizes realistic automotive radar point clouds from camera images using diffusion models. It incorporates depth, semantic, and motion cues for physical plausibility, enabling scalable multimodal simulation and improving perception models.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17897
• PDF: https://arxiv.org/pdf/2512.17897

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#AutomotiveRadar #PointClouds #DiffusionModels #ComputerVision #AutonomousDriving

✨DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

📝 Summary:
DrivingGen is the first comprehensive benchmark for generative driving world models, addressing prior evaluation gaps. It uses diverse datasets and new metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking reveals trade-offs between visua...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01528
• PDF: https://arxiv.org/pdf/2601.01528
• Project Page: https://drivinggen-bench.github.io/
• Github: https://github.com/youngzhou1999/DrivingGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yangzhou99/DrivingGen

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#AutonomousDriving #GenerativeAI #WorldModels #AIResearch #Benchmarking

✨DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

📝 Summary:
DrivingGen is the first comprehensive benchmark for generative driving world models, addressing prior evaluation gaps. It uses diverse datasets and new metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking reveals trade-offs between visua...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01528
• PDF: https://arxiv.org/pdf/2601.01528
• Project Page: https://drivinggen-bench.github.io/
• Github: https://github.com/youngzhou1999/DrivingGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yangzhou99/DrivingGen

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#AutonomousDriving #GenerativeAI #WorldModels #AIResearch #Benchmarking

✨StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving

📝 Summary:
StyleVLA is a physics-informed VLA model that generates diverse, style-aware, and kinematically plausible driving trajectories. It uses a hybrid loss and a large dataset, outperforming proprietary models like Gemini-3-Pro on specialized driving tasks.

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09482
• PDF: https://arxiv.org/pdf/2603.09482

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#AutonomousDriving #VLA #AI #DeepLearning #Robotics

✨LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset

📝 Summary:
This paper introduces KITScenes LongTail, a new dataset for long-tail driving events. It offers multi-view video, trajectories, and multilingual expert reasoning traces. This resource improves few-shot generalization and evaluates multimodal models instruction following capabilities.

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23607
• PDF: https://arxiv.org/pdf/2603.23607
• Project Page: https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail

✨ Datasets citing this paper:
• https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail

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#AutonomousDriving #ComputerVision #Datasets #LongTailLearning #MultimodalAI

✨LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset

📝 Summary:
This paper introduces KITScenes LongTail, a new dataset for long-tail driving events. It offers multi-view video, trajectories, and multilingual expert reasoning traces. This resource improves few-shot generalization and evaluates multimodal models instruction following capabilities.

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23607
• PDF: https://arxiv.org/pdf/2603.23607
• Project Page: https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail

✨ Datasets citing this paper:
• https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail

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#AutonomousDriving #ComputerVision #Datasets #LongTailLearning #MultimodalAI

✨AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing

📝 Summary:
AutoWeather4D is a 3D-aware weather editing framework that decouples geometry and illumination through a dual-pass mechanism, enabling efficient and physically accurate weather modification for autono...

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26546
• PDF: https://arxiv.org/pdf/2603.26546
• Project Page: https://lty2226262.github.io/autoweather4d/
• Github: https://github.com/lty2226262/AutoWeather4D

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#AutonomousDriving #ComputerVision #WeatherEditing #3DGraphics #AIResearch

✨DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning

📝 Summary:
DriveDreamer-Policy is a unified driving world-action model. It integrates depth, future video, and motion planning using geometry-aware world representation learning. This improves imagined futures and driving actions, achieving strong performance on navigation benchmarks.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01765
• PDF: https://arxiv.org/pdf/2604.01765
• Project Page: https://drivedreamer-policy.github.io/
• Github: https://github.com/youngzhou1999/DriveDreamer-Policy

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#AutonomousDriving #MotionPlanning #WorldModels #DeepLearning #ComputerVision