Vehicle Camera Speed-Difference Detection for Dangerous Driving
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Solution Overview
Problem
Existing vehicle sensors, such as loop coils and Orbis, are limited in versatility and cost-effective deployment, failing to provide comprehensive road coverage and lacking in sharing information about dangerous driving vehicles, especially when they move out of detection range.
Innovation Solution
A determination device using a camera-mounted time-series image analysis to estimate speed differences between an observation vehicle and a target object, determining dangerous states based on these differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed-type sensors (loop coil, Orbis) are installed to detect dangerous driving vehicles, then detection accuracy at installed positions is improved, but versatility and road coverage are worsened due to limited installation locations
Solution Approach 1:
The patent applies universality by enabling ordinary vehicles to serve dual purposes: their original transportation function plus a new function as mobile detection devices. By equipping vehicles with cameras and detection systems, any vehicle becomes a potential detection node, making the system universally applicable across all road sections without requiring specialized fixed infrastructure at every location.
Solution Approach 2:
The patent implements dynamics by transitioning from static fixed-type sensors to dynamic mobile detection systems. The detection capability moves with the vehicles themselves, allowing detection points to change continuously as vehicles travel. This dynamic approach enables coverage of entire road networks rather than being confined to predetermined fixed installation locations.
2Adaptability or versatility
If portable sensors are deployed to improve versatility, then road coverage is improved, but cost increases and deployment complexity worsens
Solution Approach 1:
The patent applies self-service by making vehicles detect and report their own dangerous driving states. The detection system leverages the vehicle's existing motion and positioning capabilities, with the vehicle itself serving as both the detected object and the detection platform. This eliminates the need for external portable sensors to be manually deployed and retrieved, significantly reducing deployment complexity.
Solution Approach 2:
The patent uses communication infrastructure (base stations, networks) as intermediaries to collect and process detection data from multiple vehicles. Rather than requiring complex manual deployment of portable sensors, the system leverages existing communication networks to aggregate information, simplifying the overall deployment while maintaining broad road coverage.
3Reliability
If detection systems are installed to identify dangerous driving vehicles, then detection capability is improved, but information sharing and tracking of moving dangers worsens due to lack of continuity when vehicles move out of detection range
Solution Approach 1:
The patent implements feedback through continuous communication between detection systems and a central server or other vehicles. When a dangerous driving state is detected, information is immediately communicated to relevant parties. This feedback loop ensures that information about moving dangers is continuously updated and shared, maintaining information continuity even as vehicles move in and out of individual detection ranges.
Solution Approach 2:
The patent applies preliminary action by proactively sharing detection information through communication networks before dangers move out of detection range or cause incidents. The system预先 communicates dangerous driving information to base stations and other vehicles, ensuring continuous awareness of moving dangers without requiring constant visual contact or overlapping detection zones.
Data Source
Figure 1
Figure 2(A)~2(B)
Figure 3
AI summary
An image acquisition unit 20 acquires a group of time-series images captured by a camera mounted at the observation vehicle. A speed difference estimation unit 24 estimates a speed difference between the target object and the observation vehicle by using a time-series change in a region representing the target object captured in the group of time-series images. The determination unit 28 determines whether the target object or the observation vehicle is in a dangerous state based on the speed difference.