Sensor Recognition Integration Device for Autonomous Driving
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Solution Overview
Problem
Existing sensor integration systems for autonomous driving, which combine data from multiple sensors of different types, face challenges in reducing processing load while maintaining accuracy, especially when targets enter the overlapping detection range of radars, leading to increased processing costs and reduced performance.
Innovation Solution
A sensor recognition integration device that includes a prediction update unit, association unit, integration processing mode determination unit, and integration target information generation unit, which predicts object behavior, calculates relationships between predicted and actual sensor data, and switches integration processing modes based on positional relationships and error covariances to optimize data integration, thereby reducing processing load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-accuracy integration processing is performed for all sensors in overlapping detection ranges, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent applies local quality by differentiating processing intensity based on spatial location. Objects in the overlapping detection range of multiple radars receive high-accuracy integration processing, while objects in non-overlapping ranges use standard processing. This spatially-variable processing quality resolves the contradiction by concentrating computational resources where they provide maximum value (overlapping regions) while reducing overall processing load.
Solution Approach 2:
The patent segments the detection space into different processing zones based on radar overlap relationships. By dividing the detection range into overlapping and non-overlapping regions, the system applies different processing strategies to each segment, enabling high accuracy where needed while maintaining overall processing efficiency.
2Measurement precision
If the number of sensors is increased to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces dynamic processing that adapts to the actual sensor configuration and detection scenarios. The integration processing mode is determined dynamically based on the positional relationship between objects and radar detection ranges, allowing the system to optimize performance for the current configuration without requiring complex fixed architectures for all possible sensor combinations.
3Measurement precision
If integration processing is performed for all detected objects, then measurement precision is improved, but processing load increases and productivity decreases
Solution Approach 1:
The patent applies local quality by selectively applying high-accuracy integration processing only to objects located in the overlapping detection ranges of multiple radars. Objects in non-overlapping ranges undergo standard processing. This selective approach ensures high accuracy for critically detected objects while maintaining overall processing throughput.
Data Source
AI summary
Provided is a sensor recognition integration device capable of reducing the load of integration processing so as to satisfy the minimum necessary accuracy required for vehicle travel control, and capable of improving processing performance of an ECU and suppressing an increase in cost. A sensor recognition integration device B006 that integrates a plurality of pieces of object information related to an object around an own vehicle detected by a plurality of external recognition sensors includes: a prediction update unit 100 that generates predicted object information obtained by predicting an action of the object; an association unit 101 that calculates a relationship between the predicted object information and the plurality of pieces of object information; an integration processing mode determination unit 102 that switches an integration processing mode for determining a method of integrating the plurality of pieces of object information on the basis of a positional relationship between a specific region (for example, a boundary portion) in an overlapping region of detection regions of the plurality of external recognition sensors and the predicted object information; and an integration target information generation unit 104 that integrates the plurality of pieces of object information associated with the predicted object information on the basis of the integration processing mode.