6 ms·
Couldn't you just mount your sensors next to an existing car's sensors, measure the car's actions then tell your network "do that" based on the mounted sensors
by superbaconman 8y ago
Couldn't you just mount your sensors next to an existing car's sensors, measure the car's actions then tell your network "do that" based on the mounted sensors input?
- deleted 8y ago[deleted]
- inverse_pi 8y agoExplaining why this doesn't work is a great ML interview question.
- alexsb92 8y agoAs someone not involved in ML, why doesn't it work?
- perfmode 8y agoreducing error on the training set doesn’t always generalize to an unseen test set
- mcny 8y agoYup, I didn't learn much from my image analysis class but the professor made sure that over fitting and under fitting concepts were firmly lodged in our heads.
- 8note 8y agoI don't want to subject my drivers to dangerous conditions to train my cars to handle them
- david-gpu 8y agoWell, there are companies out there (like drive.ai IIRC) training such models end-to-end, so it is not entirely out of the question.
- alexbeloi 8y agoIf by "do that" you mean mimic what a real driver would do for a specific set of sensor inputs, that is precisely ML tries to do. To understand what the difficulty is, it's important to consider that the size of the sensor input is very large. Don't think of it like twenty range finders around the car, rather a 360 degree medium resolution color + depth image (about 0.5 million data points coming at 30 fps). It's difficult because you will never encounter the same set of sensor inputs twice, so you can't treat it like a search space problem. Once you've accepted that, you're in AI/ML territory where you might try to reason about what the closest set of known sensor inputs and action would be (classical AI, expert system), but that is impractically difficult with as 0.5 million dimensional search space, or train an ML model to 'reason' about the sensor space to make a decision about the appropriate action. Approaches using a small number of sensors can do automatic breaking and smarter cruise control, but haven't been seen to be successful about navigating and making strategic decisions. The current belief is that more can be done by using denser sensors and more data and seems to be the case. There are people working on reducing the sensor density requirement, but the main focus right now is building a successful and safe self driving car, regardless of sensor and compute costs.
- dyarosla 8y agoWait.. 30fps?! That’s the speed of this data? I would have hoped it would be at around 90-120- at the very least 60...
- joemag 8y agoI was going to post that the impact of higher FPS is likely too low to justify needing 4x higher processing power. But then the difference in reaction time between 30fps and 120fps is O(30ms). At 60mph that translates to almost 1 meter in stoppping distance. Tough call.
- icelancer 8y agoIt isn't just stopping power. It is fidelity, confidence, and completeness of data. A camera quickly moving across a panorama opening/closing the lens every 1/30th of a second loses a ton of data compared to one opening its lens every 1/120th of a second.
- adrianN 8y agoIf it were that easy you could just strap your sensors to a normal car and let a human drive it to teach your network to "do that".