Off-Road Incident Prediction Using Underbody Camera and Terrain Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current motor vehicles lack effective systems for predicting and mitigating driving incidents in off-road situations, where vehicles may become stuck or disabled due to terrain conditions beyond their capabilities, leading to potential breakdowns or rollovers.
Innovation Solution
The implementation of an intelligent vehicle navigation system using a network of cameras, including an underbody camera, combined with machine learning models and sensor data processing, to predict potential incidents by analyzing vehicle geolocation, telemetry, and topography data, and providing real-time assistance or autonomous control to prevent such incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an intelligent vehicle navigation system with multiple cameras and machine learning models is implemented to predict off-road driving incidents, then the accuracy of incident prediction is improved, but the device complexity increases
Solution Approach 1:
The system divides the complex monitoring task into multiple specialized camera units (front, rear, side, underbody) each capturing specific terrain aspects. The machine learning model segments the analysis by processing terrain features, vehicle state, and weather conditions separately before integrating them for comprehensive incident prediction, thereby managing complexity through modular division.
Solution Approach 2:
The intelligent navigation system performs multiple functions: it monitors terrain conditions, analyzes vehicle state data, predicts potential incidents, and provides navigation guidance. The machine learning model serves as a universal processor that handles various types of input data (camera feeds, sensor readings, geolocation) and generates comprehensive safety assessments, reducing the need for separate specialized systems.
2Measurement precision
If real-time 3D topography rendering is generated using on-vehicle camera and ultrasonic sensor data, then false positives are minimized, but the use of energy increases
Solution Approach 1:
Instead of continuously processing all sensor data at maximum resolution, the system employs periodic updates of the 3D topography rendering based on changing terrain conditions and vehicle movement. The machine learning model adjusts the frequency of detailed terrain analysis based on risk assessment, reducing computational energy consumption during low-risk periods while maintaining high precision when hazards are detected.
Solution Approach 2:
The system applies partial processing by focusing computational resources on critical terrain features and high-risk areas identified by the machine learning model. Rather than rendering complete 3D topography at full resolution continuously, the system generates detailed representations only where necessary for accurate incident prediction, minimizing energy use while maintaining false positive reduction.
3Reliability
If an underbody camera is added to capture real-time images of the vehicle's undercarriage for incident prediction, then the reliability of off-road driving assistance is improved, but the device complexity increases
Solution Approach 1:
The camera system is segmented into multiple specialized units including the underbody camera, each positioned to capture specific terrain interactions. The underbody camera specifically monitors wheel contact and undercarriage terrain engagement, dividing the overall monitoring function into specialized sub-functions that together improve reliability without requiring a single overly complex system.
Solution Approach 2:
The underbody camera data is merged with inputs from other sensors (ultrasonic sensors, wheel speed sensors, terrain cameras) and processed integrally by the machine learning model. This combination of multiple data sources creates a comprehensive view of off-road conditions, improving reliability through data fusion rather than relying on any single complex sensor system.
Data Source
AI summary
Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon. The system controller commands a resident vehicle system to execute a control operation responsive to the predicted occurrence of the driving incident.