They ran over debris in the road within 60 miles.
The idea was to livestream or film a full unedited drive coast-to-coast with the vehicle driving itself at all times.
They didn’t make it out of California without crashing into easily avoidable road debris that badly damaged the Tesla Model Y:
In the video, you can see that the driver doesn’t have his hands on the steering wheel. The passenger spots the debris way ahead of time. There was plenty of time to react, but the driver didn’t get his hands on the steering wheel until the last second.
In a follow-up video, the two Tesla influencers confirmed that the Model Y had a broken sway bar bracket and damaged suspension components. The vehicle is also throwing out a lot of warnings.
Xpeng is following Tesla on FSD.
Tesla isn’t the only automaker chasing robotaxi dreams with the help of cameras and AI. Chinese automaker Xpeng, which became the world’s first automaker to install lidar into electric vehicles back in 2020, has since had a change of heart.
At the IAA Mobility 2025 show in Munich, Candice Yuan, senior director and head of product at Xpeng’s Autonomous Driving Center, told CarNewsChina that the company has grown increasingly confident in its vision-based approach since pulling lidar from its vehicles.
Xpeng’s self-driving system is called Navigation Guided Pilot (XNGP). It sounds far less polarizing than Tesla’s Full Self-Driving (FSD), which still requires constant driver supervision and readiness to take over at all times. Like Tesla, Xpeng is betting big on end-to-end machine learning models that it says could operate anywhere in China, at least theoretically.
That claim might not be entirely true. Robotaxi companies Waymo and Zoox already use lidar data to train their AI, arguing that it helps their systems read the road and environment more accurately, especially in poor lighting, bad weather, or the countless edge cases that happen in complex urban environments.
However, studies have said that training AI systems with lidar can be more complex and expensive. It can require heavy data labeling, sensor calibration and complex integration. Unless an AI system is built from the ground up to support lidar, adding it later can mean reengineering the whole system. That’s likely what Yuan meant. Not that lidar is useless, but that Xpeng’s new system wasn’t designed for it.
It seems the winner of camera vs lidar battle has not yet been determined.