Multi-Camera Vehicle Control for Preceding Vehicle Cut-In Prediction
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Solution Overview
Problem
Existing autonomous driving technologies face challenges in accurately setting travel routes for various situations, particularly in predicting and responding to the maneuvers of preceding vehicles.
Innovation Solution
A vehicle control system utilizing multiple cameras to sense the environment, calculate the orientation and speed of preceding vehicles, and control the vehicle's trajectory based on biased driving factors to maintain stability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing autonomous driving technology is used to set travel routes, then the system can operate with basic functionality, but the accuracy of travel route setting for various situations deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting the future position and orientation of the preceding vehicle based on current image information from multiple cameras. This allows the autonomous vehicle to anticipate potential maneuvers (such as lane changes or cuts) before they occur, and pre-adjust its travel route accordingly, improving both route setting accuracy and adaptability to various situations
Solution Approach 2:
The system dynamically adjusts the travel route by continuously calculating the biased driving factor based on real-time parameters including the preceding vehicle's orientation angle, transverse speed, and time to collision. This dynamic recalculation allows the system to adapt to changing situations while maintaining accurate route setting through continuous optimization
2Measurement precision
If multiple cameras are used to recognize preceding vehicles and calculate orientation, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system segments the complex task of preceding vehicle recognition into distinct functional modules: image acquisition from multiple cameras, orientation angle calculation from image differences, speed calculation in transverse direction, and biased driving factor computation. This segmentation allows each module to be optimized independently, achieving high measurement precision while managing device complexity through modular architecture
Solution Approach 2:
The multiple cameras serve multiple functions simultaneously: they capture images for recognizing the preceding vehicle's position, calculate the orientation angle through image differences, and provide data for both short-term and long-term prediction algorithms. This multi-functionality reduces the need for additional specialized sensors, thereby limiting the increase in device complexity while maintaining high measurement precision
Data Source
AI summary
A vehicle control system includes a processor that processes data related to driving of a vehicle, an imaging device to sense and image an external environment, and a vehicle controller. The processor recognizes a preceding vehicle using a plurality of cameras included in the imaging device, predicts an angle at which the preceding vehicle is oriented toward a subject line based on a difference between image information obtained from the plurality of cameras, calculates a speed of the preceding vehicle in a transverse direction based on the angle, calculates a biased driving factor based on a time remaining until the vehicle meets the preceding vehicle in a longitudinal direction and the speed of the preceding vehicle in the transverse direction, and controls the vehicle controller based on the biased driving factor.