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Autonomous Car Progress

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Maybe the rain was messing with the sensors? It appears that the Waymo car behind the Cruise car has a backup driver in it

Maybe. But if Cruise AVs cannot handle rain, then Cruise should keep a safety driver in them like Waymo has done. Waymo used safety drivers when it rained since they had not yet validated performance in all rain conditions yet. But now that Waymo knows that they can handle rain, wet roads and puddles safely and smoothly, they will likely remove safety drivers. But they kept safety drivers before when they were not sure yet.
 
Ghost Autonomy just got a permit from CPUC to allow the public to ride in their autonomous cars. They are starting with highway autonomous driving for consumer cars because they believe it is the fastest path to transforming daily driving.

"Last week, we received an autonomous vehicle permit from the California Public Utilities Commission. This means Ghost is now authorized to bring members of the public into our growing test fleet to experience autonomous driving, joining a select group of six other companies including Waymo, Cruise, and Zoox.

Ghost stands out as the first permit holder dedicated to bringing autonomous driving to consumer cars, starting on the highway. Unlike robotaxi or delivery services, Ghost makes autonomous driving for cars that people own and drive to work every day. Delivered as software, Ghost is uniquely designed for the mass market, partnering with automakers to bring autonomous driving to the mainstream.

Ghost is starting on the highway because it is the fastest path to transforming the daily driving experience with autonomy. Accounting for 62% of vehicle miles traveled, highways combine the advantages of a simpler environment with the utility of the most common form of driving.

This permit will allow for expanded demonstration of Ghost’s capabilities on California highways – an important step in our journey to make self-driving for everyone."


I had not heard of Ghost Autonomy before but apparently they are a software company that is focused on developing the AI for self-driving. Their mission is to bring self-driving to everyone by building self-driving software that can work on any average consumer car.

They say they have a breakthrough NN that can generalize and understand the physics of obstacles in motion allowing the car to avoid collisions even in the long tail of edge cases:

Ghost’s breakthrough neural networks can generalize and understand the physics of obstacles in motion regardless of type, speed, or arrangement in three-dimensional space, effectively eliminating the infinite long tail of edge cases. Like the human visual cortex, these networks have encoded the fundamental laws of physics to detect motion, avoid collisions, and find the safest path forward with extraordinary reliability.

They are starting with highway driving but plan to eventually scale to other road types:

Ghost is built for the everyday commute: Ghost is bringing attention-free self-driving to the freeway to start. Accounting for 62% of all miles traveled, freeways are simpler and more uniform driving environments, making them the ideal place to introduce and standardize new driving technology. Unlike existing robotaxi services, which operate in a few select cities, Ghost is built to transform the daily routines of millions of drivers with safe, attention-free commutes.

But freeways are just the beginning. Ghost’s neural networks are capable of expanding their operating domain beyond the freeway, adding more road types, weather conditions, and complex scenarios. With rapid training and testing processes, new capabilities can be built, validated, and deployed to continuously deliver a better and more complete driving experience for consumers across the globe, fulfilling our mission of bringing self-driving to everyone this decade.

Here is their description page:

 
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Here is an interesting video on Ghost Autonomy's physics based AI approach:


Basically, their approach is to develop AI that can detect objects in general based on shape and motion without explicitly training it to classify the object first. Put simply, they train the NN to recognize objects based on their general shape and how they move instead of training the NN to classify specific images.

They believe this is a more generalized approach because it will work on any object, even if the AI does not know what the object is. So it should work on all edge cases, even ones that you did not explicitly train for. They believe this is better than traditional computer vision that relies on object classification because it avoids that long tail problem of edge cases you have not trained on yet.

Here is the video where they discuss why they believe traditional computer vision cannot solve the long tail:

 
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Ghost Autonomy just got a permit from CPUC to allow the public to ride in their autonomous cars. They are starting with highway autonomous driving for consumer cars because they believe it is the fastest path to transforming daily driving.




I had not heard of Ghost Autonomy before but apparently they are a software company that is focused on developing the AI for self-driving. Their mission is to bring self-driving to everyone by building self-driving software that can work on any average consumer car.

They say they have a breakthrough NN that can generalize and understand the physics of obstacles in motion allowing the car to avoid collisions even in the long tail of edge cases:



They are starting with highway driving but plan to eventually scale to other road types:





Here is their description page:


Interesting. I like their use of multiple sensors and goal of hands free driving. It looks like a small garage shop with a long road ahead. One online source says they have have a sizeable $157M debt owed to investors. They'll have to sell systems for well over $2k to break even. At least they are doing their homework up front.
 
Interesting. I like their use of multiple sensors and goal of hands free driving. It looks like a small garage shop with a long road ahead. One online source says they have have a sizeable $157M debt owed to investors. They'll have to sell systems for well over $2k to break even. At least they are doing their homework up front.

They use 48 MP cameras and imaging radar.

They are obviously a very small start-up hoping their "physics based AI" will allow them to develop a cheap and generalized self-driving that they can license to automakers. The fact that they got a permit to let the public ride in their autonomous cars is encouraging that they have some sort of prototype. And I do like that they are focused on highway driving first. If their approach works, they could potentially deliver L4 highway to lots of consumer cars. That sounds like a great business model to me. Heck, I would happily pay $2k for attention-free highway driving. I don't think they are a serious threat to Waymo or even Tesla. But if they can deliver attention-free highway driving on consumer cars that would be a great product IMO.

Here is a quick video of their "attention free highway driving" in action.


And another video that explains more about their tech and approach:

 
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Here is an interesting video on Ghost Autonomy's physics based AI approach:


Basically, their approach is to develop AI that can detect objects in general based on shape and motion without explicitly training it to classify the object first. Put simply, they train the NN to recognize objects based on their general shape and how they move instead of training the NN to classify specific images.

They believe this is a more generalized approach because it will work on any object, even if the AI does not know what the object is. So it should work on all edge cases, even ones that you did not explicitly train for. They believe this is better than traditional computer vision that relies on object classification because it avoids that long tail problem of edge cases you have not trained on yet.

Here is the video where they discuss why they believe traditional computer vision cannot solve the long tail:

That's a very similar idea to the Occupancy Network Tesla is working on. The previous NN did depend on classifying specific images and that did make it so objects it didn't recognize, it wouldn't properly respond to. The ON is a lot more efficiency because it only recognizes occupied space, it doesn't have to recognize what object is there (although labels are optionally added to certain blocks).
A Look at Tesla's Occupancy Networks
 
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That's a very similar idea to the Occupancy Network Tesla is working on. The previous NN did depend on classifying specific images and that did make it so objects it didn't recognize, it wouldn't properly respond to. The ON is a lot more efficiency because it only recognizes occupied space, it doesn't have to recognize what object is there (although labels are optionally added to certain blocks).
A Look at Tesla's Occupancy Networks

Both approaches share some similarities. They are both attempts to get around the problems with classic object classification. But I think Ghost's approach is very different from occupancy networks. Occupancy networks just look at if a space is occupied or not by something you don't want to hit. Ghost is detecting objects based on their shapes and motion.
 
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I asked https://chat.openai.com/chat#
 
Zoox CEO believes autonomous driving is "one generation away from achieving critical mass" with slow and steady progress and consolidation in the mean time:

When asked when the driverless future will definitively arrive, Evans likes to suggest that autonomous driving is one generation away from achieving critical mass. Evans, who has a 17-year-old daughter and 14-year-old son, expects the technology to be widespread enough that her “kids’ kids” won’t need driver’s licenses. Until then, she predicts a slow and steady climb for the industry, with plenty of consolidation and reshuffling.

 
The Ghost speaker makes it sound as if all other systems suffer from the inability to see and react to unlabelled or unclassified objects. I would say straight away that that cannot be true. It is utterly obvious nonsense.

They want to tell us that, for example, a Tesla completely ignores an object in the street that it cannot categorize? Nobody in his right mind would program a car like that. Of course an automated car will be programmed to stop in front of an uncategorized object, rather than crash into it.

This makes the whole interview rather questionable. I won't bet on Ghost.