Vehicle Driving Guidance Using ADAS Data and Image Analysis
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Solution Overview
Problem
Current vehicle terminal devices and navigation systems lack comprehensive guidance services for predicting and mitigating accident risks during driving, and do not effectively provide legal evaluations or historical driving data for improving driver safety and convenience.
Innovation Solution
A vehicle terminal device and service server system that captures driving images and data, including ADAS information, to predict accident situations and provide legal evaluations by analyzing data before and after incidents, while also offering destination prediction and past driving history guidance services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive driving data collection and analysis systems are implemented, then accident prediction accuracy and driver safety guidance are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments driving data collection into multiple specialized modules: ADAS data acquisition module, camera image acquisition module, GPS location acquisition module, and accelerometer data acquisition module. Each module collects specific types of data independently, which are then integrated for comprehensive accident prediction analysis, reducing the complexity of a single monolithic system.
Solution Approach 2:
A service server acts as an intermediary between the vehicle terminal device and the user. The server receives raw driving data, performs complex analysis including accident risk prediction and legal evaluation, then returns processed guidance information to the driver, reducing the processing burden on the in-vehicle device.
2Reliability
If real-time driving image and data acquisition is performed, then accident situation prediction capability is improved, but energy consumption and data transmission load increase
Solution Approach 1:
The system performs data acquisition and transmission periodically rather than continuously. The vehicle terminal device collects driving data at regular intervals and transmits batches of data to the service server, reducing continuous energy consumption while maintaining adequate accident prediction capability through periodic updates.
Solution Approach 2:
The system performs preliminary data collection and local preprocessing in the vehicle terminal device before transmission. Images and sensor data are captured and pre-processed locally, with only essential information transmitted to the server, reducing transmission energy requirements while maintaining prediction accuracy.
3Loss of information
If multiple data sources including ADAS, cameras, and GPS are integrated, then comprehensive driving situation analysis is improved, but data processing time and computational requirements increase
Solution Approach 1:
Different data sources are collected and processed by dedicated modules: ADAS data by the driver assistance module, images by the camera module, location by GPS module, and acceleration by the accelerometer module. This segmentation allows parallel processing of multiple data streams, reducing overall processing time while maintaining information completeness.
Solution Approach 2:
The system implements feedback mechanisms where the service server analyzes transmitted data and returns accident risk predictions and guidance information to the vehicle terminal device. This feedback loop enables continuous monitoring and adjustment, providing timely safety guidance while optimizing processing efficiency through iterative refinement.
Data Source
AI summary
There is provided a method for providing a driving related guidance service by a service server. The method includes receiving advanced driver assistance system (ADAS) data of a vehicle related to a specific driving situation of the vehicle, location data of the vehicle, driving data of the vehicle, and a driving image captured during driving of the vehicle from a vehicle terminal device, generating guidance information related to the specific driving situation of the vehicle by analyzing the received data and the driving image, and providing a driving related guidance service for the vehicle using the generated guidance information.