Vision-Based Lateral Acceleration Prediction for Automated Driving
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Solution Overview
Problem
Automated driving systems lack the ability to predict and adjust for lateral acceleration before entering curves, which can lead to discomfort and loss of vehicle control, as they rely on sensors that do not fully replicate human visual assessment of road conditions.
Innovation Solution
A system utilizing a camera and image processing algorithms to detect road curvature and predict lateral acceleration, generating a control signal to adjust vehicle speed before entering a curve, incorporating a global positioning system and vehicle controller to reduce speed through throttle or braking adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated driving systems rely on conventional sensors (radar, cameras) for curve detection, then the system can detect road conditions, but it cannot predict lateral acceleration before entering curves, leading to discomfort and potential loss of control
Solution Approach 1:
The system performs preliminary detection of road curvature using a camera to predict lateral acceleration before the vehicle enters the curve. This advance prediction allows the control system to prepare appropriate speed adjustments, ensuring both safety and comfort by preventing sudden braking or steering corrections during curve entry
Solution Approach 2:
The patent introduces an image processing algorithm as an intermediary between the camera sensor and the vehicle control system. This intermediary processes camera images to extract road curvature information and calculate predicted lateral acceleration, bridging the gap between visual detection and control actuation
2Ease of operation
If the system reduces vehicle speed before entering curves to maintain comfort and safety, then lateral acceleration is controlled, but the system requires complex image processing and prediction algorithms
Solution Approach 1:
The patent replaces complex mechanical sensor systems with a vision-based approach using a camera and image processing algorithms. Instead of using multiple sensors to directly measure road conditions, the system uses visual information processing to infer curvature and predict lateral acceleration, simplifying the hardware while maintaining functionality
Solution Approach 2:
The system creates a virtual model of the road geometry by processing camera images to extract lane markings and calculate curvature. This virtual representation allows the system to predict future road conditions and lateral acceleration without physical contact sensors, reducing complexity while improving predictive capability
3Loss of time
If the system uses vision-based curve detection, then it can predict lateral acceleration in advance, but the system requires additional processing time and computational resources
Solution Approach 1:
The system processes only the necessary portions of the camera image data required for curve detection, rather than analyzing the entire image in detail. By focusing computational resources on extracting lane markings and calculating curvature in the relevant forward-viewing area, the system achieves timely predictions without excessive computational overhead
Data Source
AI summary
The present application relates to a method and apparatus including a sensor for detecting a first vehicle speed, a camera operative to capture an image, a processor operative to determine a road curvature in response to the image, the processor further operative to determine a first predicted lateral acceleration in response to the road curvature and the first vehicle speed, the processor further operative to determine a second vehicle speed in response to the first predicted lateral acceleration exceeding a threshold value wherein the second vehicle speed results in a second predicted lateral acceleration being less than the threshold value, and to generate a control signal indicative of the second vehicle speed, and a vehicle controller operative to reduce a vehicle velocity to the second vehicle speed in response to the control signal.