What critical technology component is Tesla's autonomous driving system lacking?

Tesla's autonomous driving system is primarily known for its reliance on computer vision and neural network processing, utilizing cameras for object detection and navigation. One critical component often discussed in the context of autonomous vehicles is lidar (light detection and ranging), which many companies use for precise distance measurements and creating detailed 3D maps of the environment.

Tesla, however, has opted to forego lidar in favor of a more vision-based approach. The belief behind this choice is that cameras can provide sufficient information for the car to navigate, and that by relying on iterative software improvements, Tesla can achieve and enhance the level of autonomy desired without lidar.

By focusing on cameras, Tesla aims to create a more scalable and cost-effective autonomous driving solution, since lidar systems can be expensive and complex to maintain. This decision has set Tesla apart in the industry, indicating a strong confidence in their technology and approach to artificial intelligence, machine learning, and data acquisition methodologies. Thus, the absence of lidar is a defining characteristic of Tesla's autonomous driving strategy.