18159612. Parking-Spot Identification and Selection Using Radar-Centric Occupancy Grid Maps simplified abstract (Aptiv Technologies Limited)
Contents
Parking-Spot Identification and Selection Using Radar-Centric Occupancy Grid Maps
Organization Name
Inventor(s)
Qian Wang of Westfield IN (US)
Jeremy S. Greene of Indianapolis IN (US)
Parking-Spot Identification and Selection Using Radar-Centric Occupancy Grid Maps - A simplified explanation of the abstract
This abstract first appeared for US patent application 18159612 titled 'Parking-Spot Identification and Selection Using Radar-Centric Occupancy Grid Maps
Abstract: This document describes techniques and systems for identifying and selecting a parking spot using radar-centric occupancy grid (RCOG) maps. An example system includes a processor that can identify available parking spots near a host vehicle using sensor data, including a radar occupancy grid (ROG) map. Parking-spot characteristics of each available parking spot are also determined using the sensor data. The processor can then determine a selected parking spot based on the parking-spot characteristics. The processor or another processor can then control operation of the host vehicle to park in the selected parking spot using an assisted-driving or autonomous-driving system and based on the parking-spot characteristics. In this way, the described system can identify, select, and navigate to a parking spot using a RCOG map without having to rely on infrastructure sensors or pass by open spots.
Key Features and Innovation:
- Utilizes radar-centric occupancy grid (RCOG) maps for identifying and selecting parking spots.
- Determines parking-spot characteristics using sensor data, including a radar occupancy grid (ROG) map.
- Enables the selection of a parking spot based on specific characteristics.
- Controls the host vehicle to park in the selected spot using assisted-driving or autonomous-driving systems.
- Reduces reliance on infrastructure sensors and improves parking efficiency.
Potential Applications: - Autonomous parking systems for vehicles. - Smart parking solutions in urban areas. - Enhanced parking assistance for drivers. - Integration into self-driving car technology. - Parking optimization in crowded environments.
Problems Solved: - Difficulty in finding suitable parking spots. - Inefficient use of parking spaces. - Lack of accurate parking guidance systems. - Challenges in autonomous parking maneuvers. - Limited visibility in crowded parking lots.
Benefits: - Improved parking efficiency. - Reduced time spent searching for parking. - Enhanced safety during parking maneuvers. - Increased utilization of available parking spaces. - Seamless integration with autonomous driving technologies.
Commercial Applications: Title: Radar-Centric Occupancy Grid (RCOG) Maps for Autonomous Parking Systems This technology can be utilized by automotive manufacturers to enhance their autonomous driving features, parking assistance systems, and overall vehicle intelligence. It can also be integrated into smart city infrastructure to improve parking management and optimize urban parking spaces.
Prior Art: Researchers interested in this technology may explore prior art related to radar-based parking assistance systems, autonomous parking technologies, and sensor fusion for parking applications.
Frequently Updated Research: Stay updated on advancements in radar sensor technology, autonomous driving systems, and smart parking solutions to further enhance the capabilities of radar-centric occupancy grid (RCOG) maps.
Questions about Radar-Centric Occupancy Grid (RCOG) Maps: 1. How does the use of RCOG maps improve parking spot selection compared to traditional methods? 2. What are the key advantages of utilizing sensor data, including ROG maps, in identifying available parking spots?
Original Abstract Submitted
This document describes techniques and systems for identifying and selecting a parking spot using radar-centric occupancy grid (RCOG) maps. An example system includes a processor that can identify available parking spots near a host vehicle using sensor data, including a radar occupancy grid (ROG) map. Parking-spot characteristics of each available parking spot are also determined using the sensor data. The processor can then determine a selected parking spot based on the parking-spot characteristics. The processor or another processor can then control operation of the host vehicle to park in the selected parking spot using an assisted-driving or autonomous-driving system and based on the parking-spot characteristics. In this way, the described system can identify, select, and navigate to a parking spot using a RCOG map without having to rely on infrastructure sensors or pass by open spots.
