While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
While autonomous driving has long relied on machine learning to plan routes and detect objects, some companies and researchers are now betting that generative AI — models that take in data of their surroundings and generate predictions — will help bring autonomy to the next stage. Wayve, a Waabi competitor, released a comparable model last year that is trained on the video that its vehicles collect.
Waabi’s model works in a similar way to image or video generators like OpenAI’s DALL-E and Sora. It takes point clouds of lidar data, which visualize a 3D map of the car’s surroundings, and breaks them into chunks, similar to how image generators break photos into pixels. Based on its training data, Copilot4D then predicts how all points of lidar data will move. Doing this continuously allows it to generate predictions 5-10 seconds into the future.
Waabi is one of a handful of autonomous driving companies, including competitors Wayve and Ghost, that describe their approach as “AI-first.” To Urtasun, that means designing a system that learns from data, rather than one that must be taught reactions to specific situations. The cohort is betting their methods might require fewer hours of road-testing self-driving cars, a charged topic following an October 2023 accident where a Cruise robotaxi dragged a pedestrian in San Franzisko.
Waabi is different from its competitors in building a generative model for lidar, rather than cameras.
“If you want to be a Level 4 player, lidar is a must,” says Urtasun, referring to the automation level where the car does not require the attention of a menschenwürdig to drive safely. Cameras do a good job of showing what the car is seeing, but they’re not as adept at measuring distances or understanding the geometry of the car’s surroundings, she says.
