Vehicle Adaptive Learning Neural Network Safety
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Solution Overview
Problem
Existing autonomous and semi-autonomous vehicle systems face challenges in determining safe paths in dynamic environments due to limitations in sensor data processing and learning from real-world experiences, leading to potential collisions and inefficiencies.
Innovation Solution
A method involving a deep neural network (DNN) with a safety agent, periodically retrained using deep reinforcement learning, processes vehicle sensor data, including color video images, to determine probabilities of unsafe operations and adapt vehicle actions such as steering, braking, and powertrain control, while integrating rule-based machine learning and simulated data for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deep neural network with periodic deep reinforcement learning retraining is used to process vehicle sensor data and determine safe paths, then the safety and decision-making accuracy of the vehicle is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training the deep neural network offline using simulated data before deployment. This pre-training establishes a robust baseline model that can handle common scenarios, reducing the need for complex real-time computations during actual vehicle operation while maintaining high safety standards.
Solution Approach 2:
The system implements periodic retraining of the neural network using deep reinforcement learning at predetermined intervals or when specific thresholds are met. This periodic update mechanism allows the system to adapt to new scenarios and improve safety over time without requiring continuous complex computations, thus balancing reliability improvement with computational feasibility.
2Adaptability or versatility
If a deep neural network is periodically retrained using deep reinforcement learning with simulated data, then the adaptability and learning capability of the vehicle system is improved, but the training time and computational resources required increase
Solution Approach 1:
The system creates virtual copies of real-world driving scenarios through simulated environments. These simulated scenarios replicate complex traffic conditions, edge cases, and rare events that are difficult to capture in real data. By training on these copied virtual scenarios, the system achieves high adaptability without requiring extensive real-world testing time, thus resolving the contradiction between learning capability and training time.
Solution Approach 2:
The system performs preliminary training actions in simulated environments before deploying the model to real vehicles. This offline pre-training phase allows the neural network to learn from a vast amount of synthetic data including rare and dangerous scenarios, establishing a strong foundation that reduces the need for extensive real-world training and accelerates deployment time.
3Measurement precision
If vehicle sensor data is processed through a deep neural network with safety agents to determine probabilities of unsafe operations, then the measurement precision of safety assessment is improved, but the processing speed and response time decrease
Solution Approach 1:
The safety assessment system is segmented into multiple specialized safety agents, each responsible for evaluating specific aspects of vehicle operation (e.g., collision risk, lane departure, pedestrian detection). This segmentation allows parallel processing of different safety dimensions, maintaining high assessment accuracy while improving overall processing speed through concurrent evaluation of multiple safety parameters.
Solution Approach 2:
The system introduces an intermediary layer that pre-processes sensor data and extracts critical features before feeding them to the deep neural network. This intermediary feature extraction stage filters out redundant information and highlights safety-critical patterns, enabling the network to achieve high measurement precision with reduced computational burden and faster processing time.
Data Source
AI summary
A computing system can determine a vehicle action based on inputting vehicle sensor data to a first neural network including a first safety agent that can determine a probability of unsafe vehicle operation. The first neural network can be adapted, at a plurality of times, by a periodically retrained deep reinforcement learning agent that includes a second deep neural network including a second safety agent. A vehicle can be operated based on the vehicle action.