Parking-Area RCCA Control for False Warning Reduction
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Solution Overview
Problem
Autonomous vehicles generate unnecessary warning signals in situations where vehicles are not at risk of collision, causing driver inconvenience and reducing the reliability of rear cross-traffic collision-avoidance assist (RCCA) functionality.
Innovation Solution
The vehicle system uses sensor and map information to determine parking area layout, identify obstacles, and control vehicle movement based on target vehicle trajectories, excluding vehicles not at risk of collision from warning signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the RCCA system generates warning signals for all detected approaching vehicles, then the safety coverage is improved, but false warnings increase causing driver inconvenience
Solution Approach 1:
The patent applies local quality by analyzing specific characteristics of target vehicles (trajectory, speed, distance to intersection) rather than treating all detected vehicles uniformly. The system selectively applies warning logic based on local conditions of each detected vehicle, generating warnings only for vehicles that meet specific collision risk criteria while ignoring others, thus maintaining safety coverage without causing false alarms.
Solution Approach 2:
The system changes parameters by introducing multiple evaluation criteria (trajectory angle, distance to intersection, relative speed) to determine whether a detected vehicle should trigger a warning. By dynamically evaluating these parameters and comparing them against threshold values, the system transforms the binary warning decision into a multi-parameter assessment, reducing false warnings while maintaining safety.
2Reliability
If the RCCA system monitors all vehicles in the surrounding area, then the detection coverage is improved, but the computational complexity and false alarm rate increase
Solution Approach 1:
The patent segments the monitoring process into distinct stages: initial vehicle detection, trajectory analysis, intersection distance calculation, and final warning decision. By dividing the complex monitoring task into modular segments, the system efficiently processes multiple vehicles without overwhelming computational complexity, as each segment handles a specific aspect of the assessment independently.
Solution Approach 2:
The system performs preliminary actions by pre-calculating trajectory parameters and intersection distances for all detected vehicles before making the final warning decision. This preliminary analysis filters out vehicles that clearly do not pose a collision risk, reducing the computational burden of the final decision-making process and lowering false alarm rates.
Data Source
AI summary
A method of controlling a vehicle comprises: obtaining, by a processor executing computer instructions stored in a memory, a location of a host vehicle and information related to surroundings of the host vehicle based on sensor information and map information; determining, by the processor, an entrance of the host vehicle into a parking area based on the location of the host vehicle and the information related to the surroundings; determining, by the processor, a layout of the parking area based on the sensor information and the map information; determining, by the processor, a location of an obstacle around the host vehicle based on the layout of the parking area, and controlling, by the processor, the host vehicle based on a movement of a target vehicle that intersects a target line formed in a longitudinal direction of the host vehicle.