Tesla's decision to omit LiDAR (Light Detection and Ranging) from its Full Self-Driving (FSD) technology is a heavily debated topic in the automotive and tech industry. This strategy is primarily based on a couple of reasons: 1. Cost-Effectiveness: LiDAR is expensive compared to cameras and radars, which Tesla uses overwhelmingly. Making self-driving technology accessible and affordable is a key part of Tesla's mission. 2. Reliability on Visual Inputs: Elon Musk, CEO of Tesla, firmly believes that the real world is primarily visually navigated, and hence asserts that autonomous vehicles should be engineered to rely heavily on visual inputs, much like humans do. Tesla's FSD tech thus heavily relies on its advanced neural network and computer vision system. 3. Software Advancements: Tesla is focusing on developing advanced AI and machine learning techniques to interpret visual data effectively, mimicking human-like driving. 4. Data Collection: With close to a million Teslas on the road, each one collecting data, Tesla has a significant advantage in terms of data volume for training their neural networks. However, this decision doesn't come without risks: 1. Limitations of Camera Systems: Inclement weather and low light conditions can compromise the effectiveness of camera-based systems, areas where LIDAR excels. 2. Dependence on AI: The success of Tesla's approach heavily relies on their ability to continually improve their machine learning algorithms. Most vehicle manufacturers use a combination of cameras, radar, ultrasound, and LiDAR for their autonomous vehicle systems. Hence, whether Tesla's bet on camera and radar-based system is smart or not largely depends on how effective their AI software becomes at analyzing real-world driving scenarios without the need for LiDAR. It’s also important to note that technology, and regulatory acceptance of this technology, are both still in development and subject to change.