Radar Corridor Prediction Using Segmented Detection Point Encoding
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Solution Overview
Problem
Existing driving assistance systems, particularly those utilizing radar sensors, face challenges in processing detection points due to their randomness, which hinders effective prediction and control of vehicle trajectories.
Innovation Solution
A computer-implemented method that encodes detection points from radar sensors into latent features, determines fixed features for segments, and calculates spatial correlations to indicate driving corridor characteristics, using machine learning modules for segment and correlation encoding, enabling improved processing and utilization of radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar detection points are used directly for prediction, then the system can process raw sensor data, but the randomness of radar data reduces prediction accuracy
Solution Approach 1:
The region of interest is divided into multiple segments, and detection points are processed segment by segment. This segmentation allows the system to handle radar data in organized units, applying encoding and aggregation operations to reduce randomness while preserving spatial information for accurate prediction.
Solution Approach 2:
The patent introduces intermediate processing steps including encoding detection points into latent features, determining fixed features for segments, and encoding fixed features to obtain spatial correlations. These intermediary representations transform raw random radar data into structured information suitable for prediction.
2Loss of information
If all detection points are processed individually, then detailed object information is preserved, but the system complexity increases
Solution Approach 1:
Detection points within each segment are merged through encoding into latent features, and fixed features are determined by aggregating latent features. This merging reduces the number of individual processing units while retaining essential object information through compressed representations.
Solution Approach 2:
The patent transforms detection points from spatial coordinates into latent feature space through encoding, and then aggregates these features to determine fixed features. This dimensional transformation allows information retention while simplifying subsequent processing operations.
3Measurement precision
If the region of interest is divided into many segments, then spatial correlation accuracy improves, but the processing time increases
Solution Approach 1:
The region of interest is divided into segments to capture spatial correlations at an appropriate granularity. This segmentation strikes a balance between achieving sufficient spatial accuracy and maintaining reasonable processing throughput by avoiding excessive segmentation.
Solution Approach 2:
The patent processes segments in a continuous pipeline, encoding detection points and determining fixed features for each segment sequentially. This continuous processing minimizes idle time and maintains efficient data flow through the system while achieving accurate spatial correlations.
Data Source
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AI summary
The subject disclosure pertains to advanced driving assistance systems and autonomous driving systems for vehicles. For example, the subject disclosure provides a computer-implemented method for a driving assistance system of a vehicle. The method comprises determining a plurality of detection points obtained by a sensor, the detection points being associated with a plurality of segments of a region of interest for the vehicle and representing objects in the plurality of segments. The method further comprises, for each segment of the plurality of segments, encoding the plurality of detection points associated to said segment into a latent feature of said detection point, and determining a fixed feature for said segment based on the latent features of the plurality of detection points. The method further comprises encoding the fixed features of the plurality of segments to obtain a spatial correlation of static detection points among the segments, the spatial correlation of static detection points indicating a characteristic of a driving corridor for the vehicle. The spatial correlation of static detection points is then provided to the driving assistance system.