Core Insights - The commercialization of autonomous driving is accelerating globally, with companies like Waymo and Baidu Apollo significantly increasing their fleets and service offerings [1][2] - Despite the apparent maturity of technology, there are still unresolved debates regarding sensor solutions and system architectures that will shape the future of autonomous driving [3][4] Sensor Solutions - There are two main camps in the sensor debate: pure vision and multi-sensor fusion, each with its own advantages and challenges [4][9] - The pure vision approach, championed by Tesla, relies on cameras and deep learning algorithms, offering lower costs and scalability, but struggles in adverse weather conditions [7][9] - Multi-sensor fusion, favored by companies like Waymo and NIO, emphasizes safety through redundancy, combining various sensors to enhance reliability [9][10] Sensor Types - LiDAR is known for its high precision in creating 3D point clouds but comes with high costs, making it less accessible for mass commercialization [11][13] - 4D millimeter-wave radar offers advantages in adverse weather conditions but lacks the resolution of LiDAR, leading to a complementary relationship between the two technologies [13][15] Algorithmic Approaches - The industry is divided between modular and end-to-end algorithm designs, with the latter gaining traction for its potential to optimize performance without information loss [16][18] - End-to-end models, while promising, face challenges related to traceability and safety, leading to the emergence of hybrid approaches that seek to balance performance and explainability [18][22] AI Models - The debate continues between Visual Language Models (VLM) and Visual Language Action Models (VLA), with VLM focusing on interpretability and VLA on performance optimization [19][21] - VLM is currently more widely adopted among major companies due to its maturity and lower training costs, while VLA is explored by companies like Tesla and Geely for its advanced reasoning capabilities [25][26] Industry Trends - The ongoing technological debates are leading to a convergence of ideas, with sensor technologies and algorithmic approaches increasingly integrating to enhance the capabilities of autonomous driving systems [25][26]