Elon Musk, the CEO of Tesla, has been a vocal critic of using Lidar technology for self-driving vehicles. However, Li Xiang, the CEO of Li Auto, believes that Musk might have a change of heart if he were to experience driving on China’s highways at night.
Li Xiang emphasized the importance of Lidar in making autonomous driving systems safer in China. He pointed out that driving on Chinese highways at night poses unique challenges, such as large trucks with broken taillights parked on the main road. In such scenarios, Lidar’s extended detection range of 200 meters provides a significant safety advantage over cameras, which can only see up to 100 meters.
The Li Auto CEO highlighted that most Chinese automakers pursuing autonomous driving rely on Lidar technology. He emphasized that Lidar plays a crucial role in ensuring the safety of passengers, especially in family-oriented vehicles. Li Xiang reiterated his company’s commitment to using Lidar in future models to prioritize the safety of everyone on the road.
In contrast, Elon Musk has been a staunch advocate of Tesla Vision, a self-driving technology that utilizes cameras and artificial intelligence without the need for Lidar. Musk has often referred to Lidar as a “crutch” and has expressed confidence in Tesla’s ability to achieve fully autonomous driving without relying on the sensor.
Despite Musk’s skepticism towards Lidar for autonomous driving, he has acknowledged its value in other applications, such as SpaceX’s Dragon capsule. However, he remains firm in his belief that Lidar is unnecessary for achieving autonomous driving in Tesla vehicles.
As the debate between Musk and proponents of Lidar technology continues, the contrasting approaches highlight the diverse perspectives within the autonomous driving industry. While Musk’s vision for self-driving technology leans towards camera-based systems, Li Xiang’s insights from driving in China underscore the practical benefits of Lidar in challenging driving conditions. Ultimately, the coexistence of different technological approaches reflects the ongoing evolution and innovation in the autonomous driving sector.