Raw LLM Responses

@vireaknou8835 The Tesla cameras actually have better dynamic range in dark and with high contrast like headlights/sun glare than human eyes. If headlights caused the cameras to lose vision there would be a lot of Teslas crashing at night. On the other hand, as us humans get older they have increasing difficulty with seeing at night. The assumption many like to make is that the problem with autonomous vehicles is perception (not seeing something in time or being able to accurately judge it's distance/position/vector). Perception is rarely an issue for autonomous vehicles - certainly less so than humans that have only vision and hearing. The difficulty with self-driving is largely with the brain portion - interpreting the sensor data to determine what every object is, what their intentions are, how to react accordingly. Radar is very low resolution compared to vision. It is also subject to false reflections and other technical issues. It likely doesn't help the perception enough to justify the compute and power that it takes away from the neural nets. The same argument goes for lidar. Increasing perception is not the issue. Being able to have an AI model that can interpret, have memory, plan ahead, and execute the driving actions in a comfortable and courteous manner are where the problem deserves the current focus. Ultrasonics are good for close range at slower speeds (i.e. parking) - but vision works quite well there too. The computation overhead of traditional vision you describe is not how Tesla's FSD works. They went that direction for a while - and they still use that for the display on the screen - but FSD (since v12) is end to end neural net ... from raw camera data in to raw driving actions out. Tesla's FSD has the lowest power consumption for the sensors and compute for any system that comes close to its capabilities. It has been tested to use around 72-160W (AI3 vs AI4). This will increase with future Tesla compute - but it will still be a fraction of other current systems like Waymo.