Core Insights - Xiaomi has made significant strides in the autonomous driving sector since the establishment of its automotive division in September 2021, with plans to release the Xiaomi SU7 in March 2024 and the YU7 in June 2025 [2] - The company is actively engaging in advanced research, with a focus on integrating cutting-edge technologies into its autonomous driving solutions, as evidenced by a substantial number of research papers published by its automotive team [2] Research Developments - The AdaThinkDrive framework introduces a dual-mode reasoning mechanism in end-to-end autonomous driving, achieving a PDMS score of 90.3 in NAVSIM benchmark tests, surpassing the best pure vision baseline by 1.7 points [6] - EvaDrive presents an evolutionary adversarial policy optimization framework that successfully addresses trajectory generation and evaluation challenges, achieving optimal performance in both NAVSIM and Bench2Drive benchmarks [9] - MTRDrive enhances visual-language models for motion risk prediction by introducing a memory-tool synergistic reasoning framework, significantly improving generalization capabilities in autonomous driving tasks [13][14] Performance Metrics - The AdaThinkDrive framework has shown a 14% improvement in reasoning efficiency while effectively distinguishing when to apply reasoning in various driving scenarios [6] - EvaDrive achieved a PDMS score of 94.9 in NAVSIM v1, outperforming other methods like DiffusionDrive and DriveSuprim [9] - The DriveMRP-Agent demonstrated a remarkable zero-shot evaluation accuracy of 68.50% on real-world high-risk datasets, significantly improving from a baseline of 29.42% [15] Framework Innovations - ReCogDrive combines cognitive reasoning with reinforcement learning to enhance decision-making in autonomous driving, achieving a PDMS of 90.8 in NAVSIM tests [18] - The AgentThink framework integrates dynamic tool invocation with chain-of-thought reasoning, improving reasoning scores by 53.91% and answer accuracy by 33.54% in benchmark tests [22] - ORION framework effectively aligns semantic reasoning with action generation, achieving a driving score of 77.74 and a success rate of 54.62% in Bench2Drive evaluations [23] Data Generation Techniques - Dream4Drive introduces a 3D perception-guided synthetic data generation framework, significantly enhancing the performance of perception tasks with minimal synthetic sample usage [26] - The Genesis framework achieves joint generation of multi-view driving videos and LiDAR point cloud sequences, enhancing the realism and utility of autonomous driving simulation data [41] - The Uni-Gaussians method unifies camera and LiDAR simulation, demonstrating superior simulation quality in dynamic driving scenarios [42]

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