Tesla(US:TSLA) 3 6 Ke·2025-06-20 02:57Core Viewpoint - The article discusses Tesla's approach to intelligent assisted driving, emphasizing the importance of a pure vision-based solution and the foundational principles behind it, as articulated by Elon Musk [1][3][20]. Group 1: Intelligent Assisted Driving Technology - Tesla's intelligent assisted driving system is based on the "first principles" theory, which relies on human visual perception and extensive real-world data to train neural networks for generalized driving decisions [3][4]. - Musk criticizes the application of LiDAR technology in intelligent assisted driving, arguing that the road system is not designed for laser emissions, leading to potential conflicts in data interpretation between cameras and LiDAR [3][19]. - The core mission of intelligent assisted driving is to ensure safety, comfort, and efficiency in transportation, which should be standardized across all vehicles, similar to the basic functionality of mobile phone communication [4][10]. Group 2: Tesla's Development Phases - Tesla's journey in intelligent assisted driving can be divided into three phases: external collaboration (2014-2016), transition to self-research (2016-2019), and full self-research (2019-present) [6][8]. - During the first phase, Tesla partnered with Mobileye and NVIDIA, initially relying on external hardware and software solutions before moving towards self-research [6][7]. - The second phase saw Tesla develop its own Full Self-Driving (FSD) chip, significantly increasing computational power from 10 TOPS to 144 TOPS, enhancing the system's capabilities [7][8]. Group 3: Data and Algorithm Development - Tesla's intelligent driving system is driven by three key elements: algorithms, data, and computing power, with a focus on data collection from production vehicles and extensive annotation efforts [10][11]. - The perception task involves recognizing and understanding the external environment, with Tesla's cameras capturing 2D images that are processed into a 3D vector space for better situational awareness [11][14]. - The introduction of the BEV (Bird's Eye View) algorithm enhances the visual perception network, allowing for a comprehensive understanding of the vehicle's surroundings [13][14]. Group 4: Challenges and Controversies - Tesla's pure vision approach has faced significant scrutiny, particularly regarding its ability to recognize static objects, leading to accidents where the system failed to react appropriately [18][19]. - The limitations of the pure vision system include challenges in adverse weather conditions, where camera performance can degrade, impacting the system's reliability [20][21]. - The debate between pure vision and multi-sensor fusion approaches continues, with each having its own advantages and challenges, highlighting the ongoing uncertainty in the intelligent driving landscape [23].