18185397. INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
- 1 INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Vehicle Safety Technology
- 1.13 Original Abstract Submitted
INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Rohit Gupta of Santa Clara CA (US)
Amr Abdelraouf of Foster City CA (US)
Kyungtae Han of Palo Alto CA (US)
INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18185397 titled 'INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL
Simplified Explanation
The patent application describes a method for a vehicle to maintain a safe distance from a lead vehicle on the road by using sensor data and machine learning to predict the recommended gap distance.
Key Features and Innovation
- Obtaining sensor data from the vehicle when traveling behind a lead vehicle.
- Determining the size of the lead vehicle.
- Predicting a recommended gap distance based on the sensor data and lead vehicle size.
- Notifying the vehicle of the recommended gap distance.
Potential Applications
This technology can be applied in autonomous driving systems, adaptive cruise control, and collision avoidance systems in vehicles.
Problems Solved
This technology addresses the issue of maintaining a safe distance from lead vehicles on the road, reducing the risk of accidents and improving overall road safety.
Benefits
- Enhanced safety on the road.
- Improved efficiency in vehicle spacing.
- Potential reduction in accidents and collisions.
Commercial Applications
- Automotive industry for implementing advanced driver assistance systems.
- Transportation companies for fleet management and safety protocols.
Prior Art
Readers can explore prior patents related to autonomous driving systems, adaptive cruise control, and collision avoidance technologies to understand the evolution of this field.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms for autonomous vehicles and sensor technologies for road safety applications.
Questions about Vehicle Safety Technology
How does this technology improve road safety?
This technology enhances road safety by using sensor data and machine learning to predict and maintain a safe distance from lead vehicles, reducing the risk of accidents.
What are the potential commercial applications of this technology?
The potential commercial applications include implementing advanced driver assistance systems in vehicles and enhancing safety protocols in transportation companies.
Original Abstract Submitted
An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.
