Vehicle Collision Warning System Using Multi-Sensor Segmentation
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Solution Overview
Problem
Current technologies inadequately provide timely detection and warning to drivers of potential collisions with road obstacles, such as pedestrians, cyclists, or vehicles, to enhance road safety.
Innovation Solution
A computer system integrated into vehicles, comprising sensors for measuring vehicle parameters and an image-acquisition device, processes data to predict potential collisions and signals the driver through acoustic, visual, or haptic warnings, using a processing unit and signalling system to assess risk levels and alert the driver accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a computer system with sensors and image-acquisition devices is implemented to detect potential collisions, then the detection capability and driver warning capability are improved, but the device complexity increases
Solution Approach 1:
The detection system is segmented into multiple independent sensor units (image-acquisition devices, radar, lidar, ultrasonic sensors) that can be selectively activated based on driving conditions. Each sensor type targets specific detection scenarios, allowing the system to achieve comprehensive detection capability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The computer system is designed to perform multiple functions: detecting stationary obstacles, moving obstacles, pedestrians, cyclists, and animals; determining collision risk; and providing driver warnings. By integrating these diverse detection and warning functions into a single multi-functional system, the patent reduces overall system complexity while improving comprehensive detection capability.
2Measurement precision
If the system processes multiple parameters including vehicle displacement, driver behavior, and obstacle position to predict collisions, then the measurement precision and prediction accuracy are improved, but the computing resources and processing time increase
Solution Approach 1:
The system performs preliminary classification of detected objects into categories (stationary obstacles, moving obstacles, vulnerable road users) and pre-calculates risk levels based on basic parameters before full collision prediction is required. This preliminary processing reduces the computational burden during critical decision-making moments while maintaining high prediction accuracy.
Solution Approach 2:
The system applies different levels of processing intensity based on situation urgency. In normal conditions, it processes essential parameters with standard computation. When collision risk increases, it activates full multi-parameter analysis including detailed trajectory prediction and driver behavior assessment, ensuring high accuracy only when computationally necessary.
3Reliability
If the system provides timely warning to the driver through multiple signalling methods, then the road safety is improved, but the ease of operation decreases due to multiple warning modes
Solution Approach 1:
The warning system dynamically adapts its signaling mode based on the detected situation and driver response. It begins with subtle warnings (visual indicators) and progressively intensifies (acoustic alarms, haptic feedback) as collision risk increases or if the driver does not respond to initial warnings. This dynamic adaptation maintains ease of operation by using minimal intervention while ensuring road safety through escalating warnings when necessary.
Solution Approach 2:
The system continuously monitors driver responses to warnings and adjusts subsequent signaling accordingly. If the driver takes evasive action, the system reduces or cancels warnings. If the driver ignores initial warnings, the system intensifies the signaling. This feedback mechanism simplifies operation by adapting to driver behavior while maintaining safety through persistent warnings when needed.
Data Source
AI summary
Described herein is a method for determining and signalling to a driver of a motor vehicle a condition of danger deriving from a potential collision of the motor vehicle itself with an obstacle. The method is able to: determine a path of travel of the motor vehicle along a stretch of road; define an area of detection of the obstacles in the stretch of road; detect the obstacles present in the area of detection in such a way as to generate a list of possible obstacles that might be hit; identify, from among the possible obstacles detected, a main obstacle; determine a level of risk associated to a condition of impact of the motor vehicle with the main obstacle detected; and finally generate a signal or alarm message regarding the level of risk determined.