"I think they have a very sort of rigid point of view, I guess, on different sensor modalities, which I don't think is fully explainable just from an engineering point of view,"
"On the good side, they've really sort of pushed the OEMs forward in the sense that they took a very ML-based approach early on,"
"In cars, it's friggin stupid. It's expensive and unnecessary,"
"Once you solve vision, it's worthless. So you have expensive hardware that's worthless on the car."
Rivian's head of autonomy criticizes Tesla's rejection of LiDAR as a rigid stance that contradicts engineering realities and ignores declining top-tier sensor costs. Tesla's early machine-learning emphasis accelerated OEM adoption of ML-based autonomy but produced a mixed approach overall. Elon Musk repeatedly characterizes LiDAR as expensive, unnecessary, and redundant once vision is solved. Industry voices emphasize the value of diverse sensor modalities and note falling LiDAR prices that increase practicality for laser-based distance sensing. The debate underscores tension between pure vision strategies and multimodal sensor architectures for achieving safer, more robust autonomous driving.
Read at Business Insider
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