Tesla – On the radar

Tesla’s actions speak louder than Musk’s words.

• Tesla is admitting that it has failed to solve the machine vision problem by returning radar to its vehicles, in a move that I suspect will be followed by lidar supporting my long-held view that in autonomous driving, Tesla is second rate at best.
• Tesla started removing radar from its vehicles in May 2021 as a way to reduce costs as it believed that its machine vision system was so good that it did not need radar to help its vehicles to perceive the road.
• Most autonomous driving players use three sensor types, lidar, cameras and radar because each of these produces a different data set that when combined produces a more reliable picture of the vehicle’s surroundings.
• However, Tesla had so much confidence in its camera-based machine vision that it felt that it did not need the other two meaning that it could reduce vehicle build costs by removing them.
• Tesla’s autopilot performance has obviously suffered as a result which is why Tesla recently informed the FCC that it would be marketing a new radar product next month (see here).
• Given the number of high-profile accidents and investigations that have ensued, I am not that surprised.
• Mobileye, who I would back over Tesla any day of the week when it comes to camera-only machine vision, is still working with lidars in its vehicles in order to reduce the rate at which the machine makes mistakes.
• The predominant method by which an autonomous driving system perceives its surroundings is through deep learning which in reality, is nothing more than a sophisticated statistical pattern recognition system.
• This means that the system has no causal understanding of what it is doing and as such, is unable to think outside of the box.
• The net result is that whenever a situation arises that the machine has not been explicitly taught, it will fail to correctly interpret the situation which will cause the vehicle to make a driving mistake.
• This makes deep learning systems very good at tasks where the data set is both finite and stable but catastrophically bad at anything where the environment keeps on changing or is random in nature.
• The dataset for the road is virtually infinite and it is changing all the time which is why Tesla (and everyone else) has continued to struggle with this problem.
• I am sure that Tesla is aware of this issue, but it also subscribes to the OpenAI philosophy of AI which is that any problem can be solved with enough data and enough compute power (see here).
• Unfortunately, this approach has yet to come close to solving the autonomous driving problem which is why I suspect another approach is needed (see here).
• The use of multiple sensor types can help to reduce the error rate because each of them sees the road differently and detects different characteristics.
• I also think that a map of what the road looks like is also highly beneficial as it reduces the uncertainty of the task as well as reduces the processing that is required to perceive the environment correctly.
• Tesla’s move to return radar to its models is an admission that its camera system is not good enough and that it needs other sensors to reduce the error rate.
• This puts it behind the competition when it comes to autonomous driving and supports my long-held view that Tesla will not be first to market.
• This means that the robotaxi strategy for which success is required to make sense of Tesla’s crazy valuation looks like it will fail to deliver the numbers promised.
• This makes the shares look even more overvalued and I suspect that there is still a long way for them to fall even from here.
• I continue to think that the best way to invest in EVs is nuclear power which is going to be needed to provide the base load power to charge all of these vehicles that we are going to buy.