Vehicle Blind-Spot Object Tracking via Predictive Movement Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle sensor systems often have blind-spots where detection gaps occur, failing to observe objects moving into and out of these regions, leading to incomplete object tracking and potential safety hazards.
Innovation Solution
Classifying detected objects by type and assigning a movement model that predicts their location and movement through detection gaps, using sensor data and map information to adjust probabilities based on road features, allowing for extrapolation when objects are out of detection range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor detection zones are used to detect objects, then detection precision is improved in covered areas, but detection reliability deteriorates in blind-spot regions
Solution Approach 1:
The system performs preliminary classification of detected objects by type (motor vehicle, cyclist, pedestrian) and assigns appropriate movement models before objects enter blind spots. This advance preparation enables continuous tracking prediction even when objects are not directly detected, maintaining reliability across detection gaps.
Solution Approach 2:
Movement models serve as intermediaries between detected objects and blind-spot regions. These models use location and movement variables from detected objects to predict positions in undetected areas, bridging the gap between sensor coverage zones and maintaining continuous object tracking reliability.
2Device complexity
If simple linear movement models are used for prediction, then device complexity is reduced, but measurement precision deteriorates in predicting object locations through blind-spots
Solution Approach 1:
Different movement models are assigned to different object types based on their specific movement characteristics. Motor vehicles, cyclists, and pedestrians each receive tailored movement models that reflect their typical acceleration patterns, speed ranges, and maneuvering behaviors, improving prediction accuracy without uniformly increasing system complexity.
Solution Approach 2:
The system adapts movement model parameters based on object classification and detected movement variables. By adjusting parameters such as acceleration rates, maximum speed, and turning behavior according to object type and observed motion, the system achieves higher prediction precision while maintaining manageable model complexity.
3Device complexity
If detection gaps are left unobserved, then device complexity is minimized, but loss of information increases regarding objects in blind-spot regions
Solution Approach 1:
The system creates virtual copies of detected objects using movement models to represent their likely positions in blind-spot regions. These predicted object representations maintain essential information about object presence, type, and probable location, compensating for the lack of direct sensor observation without adding physical sensors.
Solution Approach 2:
By classifying objects and assigning movement models before they enter detection gaps, the system prepares prediction data in advance. This preliminary action ensures that when objects transition into blind spots, their information is already encoded in movement models, preventing information loss rather than attempting to recover it later.
Data Source
AI summary
A method for detecting moving objects in the area surrounding a vehicle by means of a vehicle-mounted sensor system having separate detection zones between which there is at least one detection gap. A crossover of an object between two detection zones across a detection gap is bridged by prediction by means of a transfer algorithm. Detected objects are classified according to type and the transfer algorithm is a movement model selected to match the determined object type and according to which the object is expected to move through the gap and which comprises a probability of the object being located in the gap. The probability is calculated from location and movement variables of the vehicle and of the detected object. The probability further takes into account any features in the roadway section that may permit the object to enter and/or exit the roadway directly into/out of the detection gap.