Multi-Camera Vehicle Control for Predictive Cut-In Avoidance
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Solution Overview
Problem
Existing autonomous driving technologies face challenges in accurately setting travel routes for various situations, particularly in handling interactions with preceding vehicles that cut in front of the vehicle.
Innovation Solution
A vehicle control system utilizing multiple cameras to recognize preceding vehicles, calculate their orientation and speed, and control vehicle steering based on biased driving factors to maintain stability and accuracy in route setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing autonomous driving technology sets travel routes based on standard lane markings, then the vehicle can follow predefined paths, but it cannot accurately handle various dynamic situations such as preceding vehicles cutting in front
Solution Approach 1:
The system performs preliminary detection of preceding vehicles using multiple cameras and calculates their orientation angles and transverse speeds in advance. By computing the biased driving factor beforehand based on time remaining until meeting and transverse speed, the system prepares predictive control actions that enable accurate route setting for various dynamic situations before they fully develop
Solution Approach 2:
The system dynamically changes the travel route parameters based on the calculated biased driving factor. When a preceding vehicle is detected with potential cut-in behavior, the system adjusts the longitudinal position and transverse coordinates of the travel route by applying the biased driving factor, transforming the route from a standard predefined path to an adaptive trajectory that accounts for dynamic vehicle interactions
2Reliability
If the vehicle maintains a standard travel route without adjustment, then the control system is simple, but it cannot avoid potential cut-ins from preceding vehicles
Solution Approach 1:
The biased driving factor serves as an intermediary parameter that bridges the gap between standard lane marking-based control and adaptive situation-based control. This intermediate calculation layer processes detection data from multiple cameras and translates it into adjusted travel route coordinates, enabling safety improvements without requiring complete redesign of the control system architecture
Solution Approach 2:
The system transitions from two-dimensional lane marking-based control to three-dimensional spatial control by incorporating longitudinal position adjustments and transverse coordinate modifications. The biased driving factor enables control in the longitudinal dimension while maintaining transverse lane positioning, adding a dimension of adaptability without abandoning the structured lane-based control framework
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.