Dynamic Vehicle Region-of-Interest Detection for Collision-Priority Sensing
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) and automated driving systems (ADS) generate large amounts of data about the entire vehicle environment without distinguishing the importance of different regions, necessitating a system to identify regions of interest for enhanced object detection and collision avoidance.
Innovation Solution
A method and system using a first vehicle sensor, such as radar or LiDAR, to measure the environment, identify objects of interest, predict their paths, and determine collision probability, while a second vehicle sensor, like a camera, captures and processes images of the region of interest with adjustable resolution based on priority, utilizing machine learning for enhanced image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensors measure the entire vehicle environment, then complete environmental data is obtained, but data volume and processing complexity increase significantly
Solution Approach 1:
The patent divides the vehicle environment into multiple regions of interest (ROIs) based on collision probability assessment. Instead of processing all environmental data uniformly, the system segments the environment into high-priority ROIs (where collision probability exceeds threshold) and low-priority regions, applying different processing levels to each segment. This reduces overall processing complexity while maintaining safety-critical information completeness.
Solution Approach 2:
The patent applies different measurement and processing qualities to different spatial regions. High-priority ROIs with elevated collision probability receive higher measurement precision and more intensive processing, while low-priority regions use reduced measurement resolution. This local differentiation optimizes the balance between information completeness and processing complexity.
2Measurement precision
If all sensor data is processed at high resolution, then detection precision is maximized, but computational resources and processing time increase
Solution Approach 1:
The patent dynamically adjusts measurement precision and processing intensity based on real-time collision probability assessment. When an ROI exhibits high collision probability, the system increases measurement precision and processing depth for that region. When collision probability is low, the system reduces processing intensity. This dynamic adaptation maintains high detection precision for critical regions while improving overall processing efficiency.
Solution Approach 2:
The patent changes processing parameters (resolution, frame rate, detection sensitivity) based on the assessed collision probability of different regions. For high-priority ROIs, parameters are set to maximize detection precision; for low-priority regions, parameters are adjusted to reduce computational load. This parameter adaptation resolves the contradiction between precision and efficiency.
3Reliability
If the system monitors all regions with equal priority, then safety coverage is comprehensive, but critical collision risks may be overwhelmed by non-critical data
Solution Approach 1:
The patent extracts and isolates high-priority ROIs that exhibit collision probability above a predetermined threshold from the general environmental data stream. By taking out these critical regions for specialized processing, the system ensures they receive focused attention and resources, preventing them from being overwhelmed or lost in the larger dataset. This extraction mechanism maintains comprehensive safety coverage while prioritizing critical risks.
Solution Approach 2:
The patent implements a feedback loop where collision probability assessments continuously inform region prioritization and resource allocation. As new sensor data arrives, the system reassesses collision probabilities and dynamically adjusts which regions receive high-priority processing. This feedback mechanism ensures that emerging critical risks are quickly identified and addressed, maintaining reliable safety coverage adaptively.
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 approach allows for focused data collection and processing of critical regions, improving occupant awareness and safety by prioritizing regions with potential collisions and enhancing driver assistance features.
Implementation Method 1
The first vehicle sensor includes at least one of: a radar sensor and a light detection and ranging (LIDAR) sensor
Implementation Method 2
The first vehicle sensor includes at least one of: a radar sensor and a light detection and ranging (LIDAR) sensor
Data Source
AI summary
A method for identifying a region of interest in an environment surrounding a vehicle may include receiving interest data using a first vehicle sensor. The interest data includes a first measurement. The method further may include determining an intended path of the vehicle. The method further may include identifying the region of interest in the environment surrounding the vehicle based at least in part on at least one of: the interest data and the intended path of the vehicle. The method further may include performing a second measurement of the region of interest using a second vehicle sensor.