Light Sensor Localization for Autonomous Vehicles
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Solution Overview
Problem
Conventional LIDAR-based localization methods in autonomous driving are ineffective in rural areas with limited environmental features, vulnerable to environmental changes, and require excessive data processing, while being costly.
Innovation Solution
A system utilizing infrared light sensors with transceivers and receivers to generate and detect light signals across defined angles, allowing for cost-effective localization by determining distance and providing accurate positioning in autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR is used for localization, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent replaces expensive LIDAR devices with inexpensive light sensors (such as infrared sensors or cameras) that can provide sufficient localization data. These cheaper sensors detect light from infrastructure elements (street lights, traffic signals, building lights) to determine vehicle position, eliminating the need for costly active scanning systems while maintaining acceptable localization accuracy for rural environments.
Solution Approach 2:
The patent substitutes the mechanical LIDAR scanning system with an optical detection system using light sensors. Instead of actively emitting and scanning laser beams mechanically, the system passively detects existing light sources in the environment, replacing complex mechanical scanning components with simpler optical sensors and computational algorithms.
2Measurement precision
If LIDAR is used for localization, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential localization information from light sensor data rather than processing complete 3D point clouds. By detecting the position and characteristics of specific light sources (street lights, traffic signals) and matching them against map data, the system obtains localization accuracy sufficient for rural areas without the computational burden of full LIDAR point cloud processing.
Solution Approach 2:
The patent uses partial action by selectively detecting specific light sources rather than scanning the entire environment. The system focuses computational resources on identifying and tracking key infrastructure light elements that provide localization information, rather than processing all environmental data, thereby reducing overall computational complexity while maintaining localization precision.
3Reliability
If LIDAR is used for localization, then reliability is improved, but adaptability to environmental changes deteriorates
Solution Approach 1:
The patent employs light sensors that can detect various types of light sources (infrared, visible, different intensities) from diverse infrastructure elements (street lights, traffic signals, building lights, vehicle lights). This multi-functional detection capability allows the system to adapt to different environmental conditions and infrastructure types, maintaining localization reliability whether in urban settings with dense lighting or rural areas with sparse lighting.
Solution Approach 2:
The patent uses dynamic light detection that can adapt to changing environmental conditions. The system can adjust its detection parameters, select different light sources based on availability, and dynamically update localization calculations as the vehicle moves and environmental conditions change, providing reliable localization that adapts to both urban and rural environments rather than relying on static infrastructure assumptions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables effective localization in various environments, including rural areas, with reduced data processing requirements and lower costs compared to LIDAR systems, while maintaining accuracy and adaptability to environmental changes.
Implementation Method 1
The transceiver may be configured to generate a light signal having a plurality of bands of light across a number of defined angles
Implementation Method 2
The receiver may be configured to (i) receive the light signal and (ii) calculate a distance to the transceiver based on a detected one of the bands of light
Data Source
AI summary
A system having a transceiver and a receiver. The transceiver may be configured to generate a light signal having a plurality of bands of light across a number of defined angles. Each of the light bands relates to a height of the transceiver relative to a road. The receiver may be configured to (i) receive the light signal and (ii) calculate a distance to the transceiver based on a detected one of the bands of light. The distance is used to provide localization in an autonomous vehicle application.