Vehicle Anti-Collision System Using Sensor Merging for Obstacle Recognition
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Solution Overview
Problem
Existing vehicle collision prevention systems face challenges in accurately recognizing obstacles due to misinterpretation of shadows, sunlight, road signs, and low-illuminance environments, and struggle to detect objects beyond a certain distance or in complex scenarios like road changes or bad weather.
Innovation Solution
A vehicle system that includes an image detector, obstacle detector, and controller to acquire road type information, establish an obstacle existable region based on recognized lane information, and determine the authenticity of detected obstacles, controlling warning and braking functions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle uses image sensors to recognize obstacles, then the driver can recognize obstacles to prevent traffic accidents, but shadows or false light sources may be misunderstood as obstacles causing false alarms
Solution Approach 1:
The patent combines multiple sensor types (image sensor, LiDAR, radar, ultrasonic sensor) to perform obstacle recognition. By merging the detection results from these different sensors, the system can cross-validate observations and eliminate false alarms caused by shadows or misleading light sources, thereby improving both reliability and measurement precision simultaneously.
Solution Approach 2:
The controller acts as an intermediary that processes and integrates information from multiple sensors. It receives data from image sensors, LiDAR, radar, and ultrasonic sensors, then synthesizes this information to determine true obstacles, filtering out false detections caused by environmental factors like shadows or reflections.
2Measurement precision
If the vehicle uses distance sensors to confirm obstacle presence, then obstacle detection capability is improved, but road signs, slopes, or speed bumps may be misinterpreted as obstacles
Solution Approach 1:
The system merges data from distance sensors with image sensor data and LiDAR information. By combining these multiple data sources, the controller can distinguish between true obstacles and false targets like road signs or speed bumps, improving reliability while maintaining the enhanced detection capability provided by distance sensors.
Solution Approach 2:
The system applies partial action by selectively activating different sensor combinations based on the detection scenario. When a potential obstacle is detected, the system uses additional sensors to verify, applying the appropriate level of detection effort to avoid both false alarms and missed detections.
3Length of stationary object
If the vehicle uses LiDAR or radar sensors to recognize distant targets, then detection range is extended, but the vehicle cannot recognize roads or buildings beyond approximately 200 meters
Solution Approach 1:
The system segments the detection space into different ranges and uses different sensor combinations for each segment. For distant objects beyond 200 meters, the system uses LiDAR and radar for initial detection, then employs image sensors and navigation information to verify and identify the nature of detected objects, thereby extending effective detection range while maintaining recognition accuracy.
Solution Approach 2:
The system performs preliminary detection using LiDAR and radar to identify potential distant objects, then pre-processes this information by cross-referencing with navigation data and road information before final verification. This preliminary action allows the system to prepare for and accurately recognize distant objects that would otherwise be beyond the reliable detection range of individual sensors.
4Reliability
If the vehicle establishes obstacle existable regions based on road type information, then false alarms are reduced, but the system complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The controller serves multiple functions: it processes data from all sensor types, establishes obstacle existable regions based on road type information, performs obstacle verification, and controls warning and braking functions. By making the controller universal and multi-functional, the system reduces overall complexity despite using multiple sensors, as a single component performs all these diverse tasks.
Solution Approach 2:
The system segments the anti-collision function into distinct processing stages: road type information acquisition, obstacle detection, obstacle verification against existable regions, and control activation. This segmentation allows each stage to be handled efficiently by the controller, reducing complexity through structured processing rather than requiring separate dedicated components for each function.
Data Source
AI summary
A vehicle and a method for controlling a same are disclosed, wherein the method for controlling the vehicle may include acquiring road type information from at least one of another vehicle, a server, an infrastructure, and a user interface (UI) configured to perform a navigation function; recognizing traveling lane information on a basis of image information detected by an image detector; establishing an obstacle existable region on a basis of the recognized traveling lane information; acquiring information regarding an obstacle from among obstacles detected by an obstacle detector on a basis of the established obstacle existable region; and controlling an anti-collision function on a basis of the obstacle information.