Stop-and-Go Vehicle Guidance Using Optical Flow for Pedestrian Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing longitudinal driver assistance systems struggle to reliably detect pedestrians or cyclists as obstacles, often failing to recognize them in time or misclassifying them, which poses safety risks during automatic restart processes.
Innovation Solution
The method employs an image recording device to generate differential images by subtracting consecutive frames, and determines optical flow vectors to detect changes and movements, ensuring obstacle detection is accurate and continuous, even at low speeds, by analyzing pixel-wise signal differences and vector fields, while excluding non-threatening objects like vehicle rear lights or shadows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the existing longitudinal driver assistance system uses conventional object detection algorithms to detect obstacles, then the system can identify vehicles for longitudinal control, but the system fails to reliably detect pedestrians or cyclists as obstacles
Solution Approach 1:
The patent changes the detection parameters by switching from vehicle-specific detection algorithms to optical flow analysis parameters. The system evaluates pixel displacement patterns, movement vectors, and temporal changes in image sequences to detect obstacles, rather than relying on vehicle recognition parameters. This parameter transformation enables reliable detection of pedestrians and cyclists who do not match vehicle detection patterns.
Solution Approach 2:
The patent replaces the conventional mechanical/optical sensor-based vehicle detection system with an image processing-based optical flow analysis system. Instead of using radar or lidar sensors primarily tuned for vehicle detection, the system substitutes image recording and pixel-level optical flow calculation to detect any moving obstacle, achieving broader detection capability for pedestrians and cyclists.
2Device complexity
If the system uses the front environment sensor system primarily designed for vehicle recognition, then the system can maintain simple sensor configuration, but the classification of non-vehicle objects becomes difficult and detection is delayed
Solution Approach 1:
The patent makes the image recording device and processing system universal by designing it to detect all types of obstacles (vehicles, pedestrians, cyclists) rather than being specialized for vehicle detection only. The optical flow analysis methodology is universally applicable to any moving object, allowing the same sensor and algorithm to serve multiple detection purposes without requiring additional specialized sensors.
Solution Approach 2:
The patent introduces dynamic detection capabilities by continuously analyzing sequences of images and calculating optical flow vectors that capture movement patterns. This dynamic approach allows the system to distinguish between stationary objects (like vehicle rear lights) and moving obstacles (pedestrians, cyclists) based on their motion characteristics, improving classification accuracy without adding static sensors.
3Reliability
If the system implements continuous obstacle monitoring from standstill to prevent premature restart, then safety is improved, but the processing load and detection time increase
Solution Approach 1:
The patent implements periodic obstacle detection by continuously capturing image sequences and calculating optical flow at regular intervals during the standstill period. This periodic monitoring ensures that obstacles are detected at multiple time points, providing continuous safety verification while managing processing load through structured, interval-based analysis rather than continuous uninterrupted processing.
Solution Approach 2:
The patent performs preliminary obstacle detection and verification during the standstill period before the automatic restart is executed. By conducting optical flow analysis on image sequences captured during the waiting period, the system verifies the absence of obstacles in advance, ensuring safety is confirmed before the restart action takes place, thus preventing premature restarts without adding detection time to the restart execution itself.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Method for the automatic longitudinal guidance of a motor vehicle (1) by means of a longitudinal guidance driver assistance system (2) with Stop&Go function, comprising a detection device (4) for acquiring information concerning a vehicle ahead (1), by means of which driver assistance system the motor vehicle is automatically braked to a standstill depending on the information acquired by means of the detection device and, if the vehicle ahead starts moving again, is automatically started moving again depending on a confirmation signal that can be given by the driver via an input device (7), wherein, at least from the time of the standstill of the motor vehicle, the area in front of the vehicle is continuously monitored for any obstacles located in the area in front of the vehicle by means of a monitoring device (8), wherein the monitoring device comprises an image acquisition device (8) that continuously provides individual images showing the area in front of the vehicle,wherein, for obstacle detection, either difference images are generated and evaluated from a first single image and a second single image taken at a later time, or wherein, for obstacle detection, the vectors of the optical flow of the image information of at least a part of the pixels of two sequentially taken single images composed of pixels are determined and evaluated.