Deep Learning Road Surveillance via Vehicle Sensor Data
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Solution Overview
Problem
Current surveillance methods for road environments associated with vehicles are costly and do not effectively utilize sensor data, making it difficult to monitor and collect data efficiently.
Innovation Solution
A method, apparatus, and computer program product utilize deep learning to determine vehicle behavior by processing vehicle sensor data, predicting behavior patterns, and encoding this data for improved surveillance and autonomous driving capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional surveillance methods are used to monitor road environments, then coverage and monitoring capability are provided, but cost is high and data utilization efficiency is low
Solution Approach 1:
The patent enables vehicles to autonomously perform surveillance of their own road environment using their existing sensors (cameras, LIDAR, radar). Each vehicle serves itself by collecting, processing, and analyzing its own sensor data to determine vehicle behaviors and map road conditions, eliminating the need for expensive external surveillance infrastructure while maintaining reliable monitoring capability
Solution Approach 2:
The patent creates virtual copies of the physical road environment by generating digital representations from vehicle sensor data. Machine learning models process sensor inputs to create virtual models of vehicle behaviors and road conditions, allowing surveillance functions to be performed in the digital domain at low cost while maintaining accuracy
2Measurement precision
If more sensors are deployed to improve surveillance precision, then measurement accuracy increases, but system complexity and cost increase
Solution Approach 1:
The patent makes existing vehicle sensors multi-functional by using them not only for autonomous driving navigation but also for road environment surveillance. The same cameras, LIDAR, and radar used for basic vehicle operation are leveraged to detect vehicle behaviors, map road conditions, and provide surveillance functions, achieving high measurement precision without adding specialized surveillance hardware
Solution Approach 2:
The patent replaces complex physical surveillance infrastructure with computational models and algorithms. Instead of deploying additional physical sensors and processing hardware, the system uses machine learning models to extract surveillance information from existing sensor data, substituting mechanical/system complexity with software-based solutions
3Measurement precision
If vehicle sensor data is collected and processed to determine vehicle behaviors, then surveillance precision and confidence improve, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary processing of vehicle sensor data by continuously collecting and pre-processing sensor inputs as vehicles operate. Machine learning models are pre-trained on historical data and continuously refined, so that when surveillance analysis is needed, the system can quickly process new data with high confidence without requiring extensive real-time computational resources
Solution Approach 2:
The patent processes only the necessary portions of sensor data required for surveillance purposes rather than analyzing all available data. The machine learning models selectively extract relevant features and behaviors from sensor inputs, performing partial processing that achieves high surveillance confidence while minimizing computational overhead and processing time
Data Source
AI summary
A method, apparatus and computer program product are provided for surveillance of road environments via deep learning. In this regard, one or more features for vehicle sensor data associated with one or more vehicles traveling along a road segment proximate to a vehicle are determined. The vehicle includes one or more sensors that captures the vehicle sensor data. Furthermore, vehicle behavior data associated with the one or more vehicles is predicted using a machine learning model that receives the one or more features. The machine learning model is trained for detection of vehicle behavior based on historical vehicle sensor data and one or more rules associated with the road segment. The vehicle behavior data is also encoded in a database to facilitate modeling of vehicle behavior associated with the road segment.