Traffic Object Map Updating Using Multi-Vehicle Detection Consensus
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining accurate traffic maps due to changes in traffic objects such as signs and signals caused by construction or accidents, leading to potential safety hazards if not updated promptly.
Innovation Solution
A sensor positioning platform that dynamically repositions and rotates sensors to enhance visibility and accuracy, allowing for real-time updates of traffic maps by detecting changes in traffic objects, utilizing a combination of camera, LIDAR, and RADAR sensors with a motorized system for optimal coverage and cleaning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are mounted at fixed locations on autonomous vehicles, then the device complexity is reduced and ease of operation is improved, but the measurement precision and detection accuracy deteriorate when traffic objects change position or type
Solution Approach 1:
The patent implements a motorized sensor positioning platform that can dynamically reposition and rotate sensors to track and detect traffic objects at varying positions and orientations. This dynamic adjustment capability allows the system to maintain high measurement precision for traffic objects regardless of their location changes, directly resolving the contradiction between fixed mounting simplicity and detection accuracy.
2Loss of information
If traffic maps are not updated with real-time sensor data, then the system stability and reliability of current maps are maintained, but the loss of information increases when traffic objects change due to construction or accidents
Solution Approach 1:
The patent establishes a feedback mechanism where sensor data detecting traffic object changes is continuously fed back to update the traffic map. This closed-loop system ensures that when traffic objects change position or type, the information is captured and reflected in the updated map, preventing information loss while maintaining timely updates through automated processing.
3Measurement precision
If multiple sensors (camera, LIDAR, RADAR) are integrated with motorized positioning and cleaning mechanisms, then the measurement precision and detection capabilities are improved, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent integrates multiple sensor types (camera, LIDAR, RADAR) onto a single motorized positioning platform that serves universal functions for all sensors. The platform provides combined positioning, rotation, and cleaning capabilities for all sensor types, reducing the need for separate mounting and maintenance systems for each sensor, thereby easing manufacturing while maintaining high detection precision.
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
The solution ensures accurate and timely updates of traffic maps, enhancing the safety and navigation of autonomous vehicles by improving sensor coverage and detection capabilities, reducing the risk of accidents due to outdated information.
Implementation Method 1
An autonomous vehicle can include various sensors, such as a camera sensor, a light detection and ranging (LIDAR) sensor, and a radio detection and ranging (RADAR) sensor
Implementation Method 2
An autonomous vehicle can include various sensors, such as a camera sensor, a light detection and ranging (LIDAR) sensor, and a radio detection and ranging (RADAR) sensor
Data Source
AI summary
Systems, methods, and computer-readable media are provided for receiving traffic object data from a plurality of autonomous vehicles, the traffic object data including a geographic location of a traffic object, comparing the traffic object data of each of the plurality of autonomous vehicles with known traffic object data, determining a discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, grouping the traffic object data of each of the plurality of autonomous vehicles based on the determining of the discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, determining whether a group of traffic object data of the grouping of the traffic object data of each of the plurality of autonomous vehicles exceeds a threshold; and updating a traffic object map based on the traffic object data of the group that exceeds the threshold.