Four Autonomous Driving Technical Approaches Compete Head-to-Head

[Technical Approaches] The four mainstream autonomous driving solutions each have clear strengths and weaknesses, and the industry has entered a period of diversified, accelerated competition.

Key Trends: Pure Vision, LiDAR, Vehicle-to-Infrastructure Coordination, and End-to-End Large Models Advance in Parallel

Tesla champions the pure vision approach, which offers low costs but relies heavily on algorithms. Companies like XPeng and NIO adopt LiDAR-based sensor fusion to enhance safety. Huawei and Baidu strongly promote vehicle-to-infrastructure (V2X) coordination to reduce the intelligence burden on individual vehicles. Starting in 2026, multiple automakers are accelerating the deployment of end-to-end large models in vehicles.

Strategic Foundations: Path Selection Impacts Cost, Safety, and Deployment Timelines

The hardware cost of pure vision systems is under RMB 5,000, while LiDAR-based solutions exceed RMB 20,000. V2X coordination requires extensive infrastructure support and is unlikely to achieve widespread adoption in the short term. End-to-end large models depend on high-performance computing chips and massive datasets, with NVIDIA Orin emerging as the dominant choice.

Industry Impact: Technological Fragmentation Intensifies; Unified Standards and Regulations Are Urgently Needed

Consumers face inconsistent user experiences, prompting industry calls for a unified testing and certification framework to prevent "specification wars" from obscuring real-world capabilities.