Autonomous Vehicle Collision Avoidance via Predictive Path Reliability
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Solution Overview
Problem
Current autonomous driving systems, such as Adaptive Cruise Control (ACC), face challenges in predicting the traveling path of a target vehicle to effectively avoid collisions, particularly in dynamic and uncertain environments.
Innovation Solution
A vehicle system equipped with sensors (including cameras, radar, and Lidar) and a controller that predicts the expected traveling path of both the vehicle and the target object, determines the reliability of this path using a learning table and GPS data, and operates to avoid collisions by adjusting the vehicle's trajectory or velocity based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses multiple sensors (camera, radar, Lidar) to acquire target object information, then the measurement precision and reliability of target detection is improved, but the device complexity increases
Solution Approach 1:
The patent divides the sensing system into multiple specialized sensor modules (camera for visual information, radar for distance and velocity, Lidar for 3D mapping), where each sensor type is optimized for specific detection tasks. This segmentation allows each component to excel at its specialized function while collectively providing comprehensive target detection capability.
Solution Approach 2:
The patent integrates data from multiple heterogeneous sensor sources (camera, radar, Lidar) through a unified processing system that fuses their outputs. By merging the complementary information from each sensor type, the system achieves higher measurement precision and reliability than any single sensor could provide alone.
2Reliability
If the system predicts expected traveling path using learning tables and GPS data, then the reliability of path prediction is improved, but the loss of time for processing and calculating increases
Solution Approach 1:
The system pre-generates learning tables containing statistical information about typical traveling paths and behaviors before actual operation. During real-time path prediction, the system queries these pre-computed tables rather than calculating from scratch, significantly reducing processing time while maintaining high reliability through the use of empirically derived data.
Solution Approach 2:
The system incorporates GPS data and actual vehicle trajectory information as feedback to continuously refine and update the learning tables. This feedback mechanism allows the system to improve path prediction reliability over time by learning from actual driving patterns while maintaining efficient real-time performance.
Data Source
AI summary
A vehicle is provided to avoid a collision with a target object located in front of the vehicle by predicting an expected traveling path of the target object. The vehicle also predicts the possibility of a collision with the target object.