18185384. RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
- 1 RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT - 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 the Technology
- 1.13 Original Abstract Submitted
RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT
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)
RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18185384 titled 'RECOMMENDED FOLLOWING GAP DISTANCE BASED ON CONTEXT
Simplified Explanation
The patent application describes a system that uses sensor data from a vehicle to determine the size of a lead vehicle on the road, predict the recommended gap distance between the two vehicles, and notify the driver of the recommended gap distance.
Key Features and Innovation
- Obtaining sensor data from a vehicle traveling behind a lead vehicle.
- Determining the size of the lead vehicle.
- Predicting the recommended gap distance using a machine learning model.
- Notifying the driver of the recommended gap distance.
Potential Applications
This technology could be applied in autonomous driving systems, adaptive cruise control, and collision avoidance systems in vehicles.
Problems Solved
This technology addresses the problem of maintaining a safe distance between vehicles on the road, reducing the risk of accidents and improving overall road safety.
Benefits
- Enhanced safety on the road.
- Improved efficiency in traffic flow.
- Reduced risk of collisions and accidents.
Commercial Applications
- Automotive industry for implementing advanced driver assistance systems.
- Transportation companies for fleet management and safety protocols.
Prior Art
Readers interested in prior art related to this technology could explore research papers on machine learning in autonomous vehicles, sensor fusion in automotive systems, and adaptive cruise control technologies.
Frequently Updated Research
Researchers are continually exploring advancements in machine learning algorithms for autonomous vehicles, sensor technologies for improved data collection, and real-time communication systems for vehicle-to-vehicle interactions.
Questions about the Technology
How does this technology improve road safety?
This technology enhances road safety by providing real-time recommendations for maintaining safe distances between vehicles, reducing the risk of accidents.
What are the potential implications of this technology on traffic management?
This technology could lead to more efficient traffic flow, reduced congestion, and improved overall traffic management.
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.
