Autonomous vehicles Archives

Developing and validating robust systems controlled by artificial intelligence in a systematic and adaptable manner.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Drones, Electrical Engineering & Computer Science (eecs), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Networks, Research, Robotics, Robots, Safety, School of Engineering, UncategorizedJuly 18, 202446Views 0Likes 0Comments

Neural networks have been of immense benefit in the design of robot controllers, boosting the adaptive and effectiveness abilities of these machines. However, their complex nature makes it challenging to confirm their safe execution of assigned tasks. Traditionally, the verification of safety and stability are done using Lyapunov functions. If a Lyapunov function that consistently…

Developing and confirming robust AI-operated systems using thorough and adaptable methods.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Drones, Electrical Engineering & Computer Science (eecs), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Networks, Research, Robotics, Robots, Safety, School of Engineering, UncategorizedJuly 18, 202439Views 0Likes 0Comments

Researchers from the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an algorithm to mitigate the risks associated with using neural networks in robots. The complexity of neural network applications, while offering greater capability, also makes them unpredictable. Current safety and stability verification techniques, called Lyapunov functions, do not…

Researchers utilize shadows to create 3D scene models, incorporating objects that are normally obstructed from sight.

Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Imaging, Light, Machine learning, Media Lab, Research, School of Architecture and Planning, Sensors, UncategorizedJune 18, 202439Views 0Likes 0Comments

Researchers from MIT and Meta have developed a computational vision technique, named PlatoNeRF, that allows for creating vivid, accurate 3D models of a scene from a single camera view. The innovative technology uses the shadowing in a scene to determine what could lie within obstructed areas. By combining machine learning with LIDAR (Light Detection and…

Scientists improve side vision capability in AI systems.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 18, 202437Views 0Likes 0Comments

Scientists improve the peripheral vision capabilities in AI models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202443Views 0Likes 0Comments

Researchers at Massachusetts Institute of Technology (MIT) have developed an image dataset to simulate peripheral vision in artificial intelligence (AI) models. This step is aimed at helping such models detect approaching dangers more effectively, or predict whether a human driver would take note of an incoming object. Peripheral vision in humans allows us to see…

Scientists improve side vision capabilities in AI modules.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 17, 202440Views 0Likes 0Comments

Researchers from MIT have developed an image dataset that simulates peripheral vision in machine learning models, improving their object detection capabilities. However, even with this modification, the AI models still fell short of human performance. The researchers discovered that size and visual clutter, factors that impact human performance, largely did not affect the AI's ability.…

Scientists improve the side vision capabilities in artificial intelligence models.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202433Views 0Likes 0Comments

Peripheral vision, most humans' mechanism to see objects not directly in their line of sight, although with less detail, does not exist in AI. However, researchers at MIT have made significant progress towards this by developing an image dataset to simulate peripheral vision in machine learning models. The research indicated that models trained with this…

Scientists improve sideline sight in AI prototypes.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 16, 202437Views 0Likes 0Comments

MIT researchers are replicating peripheral vision—a human's ability to detect objects outside their direct line of sight—in AI systems, which could enable these machines to more effectively identify imminent dangers or predict human behavior. By equipping machine learning models with an extensive image dataset to imitate peripheral vision, the team found these models were better…

Scientists improve sideline viewing capabilities in AI systems.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 15, 202433Views 0Likes 0Comments

Peripheral vision, the ability to see objects outside of our direct line of sight, has been simulated by researchers at MIT to be used with AI technology. Unlike human vision, AI lacks the capability to perceive peripherally. Enhancing AI with this ability could greatly enhance its proactivity in identifying threats, and could even predict if…

Scientists improve side vision capabilities in AI systems.

Artificial Intelligence, Autonomous vehicles, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, UncategorizedJune 15, 202435Views 0Likes 0Comments

A team from MIT has created an image dataset aimed at simulating peripheral vision in machine learning models, a characteristic which AI typically lacks. This could improve the models' ability to recognise approaching threats and predict whether a human driver would spot an oncoming object. In experiments, these models improved in terms of hazard detection,…