<story><title>Tesla stopped reporting its Autopilot safety numbers online. Why?</title><url>https://www.latimes.com/business/story/2022-12-27/tesla-stopped-reporting-autopilot-safety-statistics-online</url></story><parent_chain><item><author>gamegoblin</author><text>I&amp;#x27;m kind of coming around to the idea that Comma AI&amp;#x27;s approach of full end-to-end is the right one.&lt;p&gt;Tesla, Waymo, Cruise, etc all take real world data and load it into a virtual representation, then do planning inside virtual space, then execute that plan in the real world. It seems like this is just really clunky and doesn&amp;#x27;t handle weird edges very well.&lt;p&gt;Comma AI&amp;#x27;s thesis is that creating this big virtual world to do planning in is both a waste of time and compute, and also a worse driving experience. They just take a ton of human data, and essentially say &amp;quot;given this situation, what would a human do?&amp;quot;. I.e. it&amp;#x27;s a neural network that takes in the last few frames of video, and outputs steering + accelerator + brake commands, with no intermediate virtual space representation.&lt;p&gt;A huge advantage of this approach is it scales really well. Human data in, model out. They are maybe a year behind Tesla FSD in terms of capability with a tiny team of ~15 or so, and they are gaining ground.&lt;p&gt;Tesla could probably improve their system by adopting a similar end-to-end approach, but I think this may not be easy to do for organizational&amp;#x2F;corporate political reasons. They have several hundred engineers working on all of the various bits of their FSD stack who are all incentivized to defend their turf.</text></item></parent_chain><comment><author>sho_hn</author><text>This is not good enough to handle edge cases.&lt;p&gt;It&amp;#x27;s a little bit like ChatGPT: ChatGPT is a next-word prediction engine. It has ingested and analyzed &amp;quot;human data&amp;quot; which has given it a take on what might come after a prompt. With good training data, a large enough token window and a helping of RLHF this can do very cool things. But there&amp;#x27;s no actual reasoning going on.&lt;p&gt;An &amp;quot;edge case&amp;quot; is a prompt that throws this approach for a spin, because statistics over the training data corpus don&amp;#x27;t yield a good prediction on it. For example because it&amp;#x27;s novel or its structure is uncommon. In those cases ChatGPT will do its best and confidently return something very dumb.&lt;p&gt;With the driving problem, Tesla and others are trying to mitigate this with two approaches:&lt;p&gt;- superstructures of rule-based planning to keep the predictions within safe lanes (no pun intended), i.e. bake in some reasoning after all&lt;p&gt;- trying to write simulations that auto-generate &amp;quot;edge cases&amp;quot; or just stuff that is underrepresented in the training data set and add those simulated 3d renders to editorialize the predictions&lt;p&gt;It&amp;#x27;s currently still unclear if this is enough to solve the problem or if we&amp;#x27;re still some innovations (e.g. ones that get us closer to AGI) away from being able to make it work.</text></comment>
<story><title>Tesla stopped reporting its Autopilot safety numbers online. Why?</title><url>https://www.latimes.com/business/story/2022-12-27/tesla-stopped-reporting-autopilot-safety-statistics-online</url></story><parent_chain><item><author>gamegoblin</author><text>I&amp;#x27;m kind of coming around to the idea that Comma AI&amp;#x27;s approach of full end-to-end is the right one.&lt;p&gt;Tesla, Waymo, Cruise, etc all take real world data and load it into a virtual representation, then do planning inside virtual space, then execute that plan in the real world. It seems like this is just really clunky and doesn&amp;#x27;t handle weird edges very well.&lt;p&gt;Comma AI&amp;#x27;s thesis is that creating this big virtual world to do planning in is both a waste of time and compute, and also a worse driving experience. They just take a ton of human data, and essentially say &amp;quot;given this situation, what would a human do?&amp;quot;. I.e. it&amp;#x27;s a neural network that takes in the last few frames of video, and outputs steering + accelerator + brake commands, with no intermediate virtual space representation.&lt;p&gt;A huge advantage of this approach is it scales really well. Human data in, model out. They are maybe a year behind Tesla FSD in terms of capability with a tiny team of ~15 or so, and they are gaining ground.&lt;p&gt;Tesla could probably improve their system by adopting a similar end-to-end approach, but I think this may not be easy to do for organizational&amp;#x2F;corporate political reasons. They have several hundred engineers working on all of the various bits of their FSD stack who are all incentivized to defend their turf.</text></item></parent_chain><comment><author>Avshalom</author><text>&amp;gt;&amp;gt;takes in the last few frames of video&lt;p&gt;isn&amp;#x27;t that how uber killed a woman walking a bike in phoenix? During the critical last second(s) the model didn&amp;#x27;t know what she was and kind of ignored her even though previous seconds had tagged her as a moving cyclist.</text></comment>