17873263. SYSTEMS AND METHODS FOR DETECTING LANES USING A SEGMENTED IMAGE AND SEMANTIC CONTEXT simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
SYSTEMS AND METHODS FOR DETECTING LANES USING A SEGMENTED IMAGE AND SEMANTIC CONTEXT
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Shunsho Kaku of Mountain View CA (US)
Jeffrey M. Walls of Ann Arbor MI (US)
Kun-Hsin Chen of Mountain View CA (US)
Steven A. Parkison of Ann Arbor MI (US)
SYSTEMS AND METHODS FOR DETECTING LANES USING A SEGMENTED IMAGE AND SEMANTIC CONTEXT - A simplified explanation of the abstract
This abstract first appeared for US patent application 17873263 titled 'SYSTEMS AND METHODS FOR DETECTING LANES USING A SEGMENTED IMAGE AND SEMANTIC CONTEXT
Simplified Explanation
The patent application describes a system and method for detecting lanes in a driving scene by segmenting road regions using an ontology to derive semantic context. Here are the key points:
- The method involves segmenting an image of a driving scene into lane lines and road regions based on an ontology.
- A pixel subset from the image contains lane information derived from the ontology.
- Pixel depth for the lane lines and road regions is computed using a model.
- 3D context is derived by establishing relations between the semantics and pixel depth.
- The relations infer a driving lane for a vehicle based on the types of lane lines and road regions adjacent to it.
- The method also includes executing a task to control the vehicle on the driving lane using the 3D context.
Potential applications of this technology:
- Autonomous driving systems: The technology can be used in autonomous vehicles to accurately detect and navigate lanes, enabling safer and more efficient driving.
- Advanced driver assistance systems (ADAS): The system can enhance ADAS features like lane departure warning and lane keeping assist by providing more accurate lane detection and context-aware control.
- Traffic management: The technology can be used in traffic monitoring systems to analyze lane usage patterns and optimize traffic flow.
Problems solved by this technology:
- Accurate lane detection: By using an ontology and semantic context, the system can accurately segment lane lines and road regions, even in challenging driving conditions.
- Context-aware control: The derived 3D context allows for more precise control of the vehicle on the driving lane, improving safety and performance.
Benefits of this technology:
- Improved safety: Accurate lane detection and context-aware control contribute to safer driving by reducing the risk of lane departure or collision.
- Enhanced efficiency: The technology enables more efficient lane navigation, leading to smoother traffic flow and reduced congestion.
- Better user experience: By providing reliable lane information, the system enhances the user experience of autonomous driving and ADAS features.
Original Abstract Submitted
System, methods, and other embodiments described herein relate to the detection of lanes in a driving scene through segmenting road regions using an ontology enhanced to derive semantic context. In one embodiment, a method includes segmenting an image of a driving scene, independent of maps, by lane lines and road regions defined by an ontology and a pixel subset from the image has semantics of lane information from the ontology. The method also includes computing pixel depth from the image for the lane lines and the road regions using a model. The method also includes deriving 3D context using relations between the semantics and the pixel depth, the relations infer a driving lane for a vehicle from types of the lanes lines and the road regions adjacent to the driving lane. The method also includes executing a task to control the vehicle on the driving lane using the 3D context.
