Autonomous Driving Control Model Using Deep Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous driving systems require complex operations to determine control signals, which can be inefficient and may not optimize driving decisions in real-time.
Innovation Solution
A system that uses a trained control model, such as a Deep Deterministic Policy Gradient (DDPG) network model, to directly determine control signals based on vehicle state and driving information, selecting the most appropriate signal for transmission to the vehicle's control components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a series of operations (obtaining driving information, determining driving action, planning driving path, determining control signal) is performed to determine control signals, then the autonomous driving system can make comprehensive decisions, but the system complexity and operation time increase
Solution Approach 1:
The patent merges multiple sequential operations (obtaining driving information, determining driving action, planning driving path, and determining control signal) into a single integrated deep reinforcement learning model. The model directly maps driving information to control signals, eliminating the need for separate modules for each operation while maintaining comprehensive decision-making capabilities through the learned policy.
Solution Approach 2:
The patent introduces a deep reinforcement learning model as an intermediary that processes driving information and directly outputs control signals. This intermediary model replaces the complex multi-step decision-making pipeline, learning the optimal mapping from sensory inputs to control actions through reinforcement learning training.
2Reliability
If a series of operations is performed to determine control signals, then comprehensive driving decisions can be made, but the time efficiency decreases
Solution Approach 1:
The patent performs preliminary training of the deep reinforcement learning model offline using extensive simulation data and real-world driving scenarios. During actual autonomous driving operation, the pre-trained model can directly infer control signals without performing the full sequence of operations, significantly reducing real-time computation time while maintaining decision quality.
Solution Approach 2:
The patent replaces the mechanical sequential processing system (multiple discrete operations executed in sequence) with a neural network-based parallel processing system. The deep reinforcement learning model processes driving information through parallel computational layers, enabling faster inference compared to sequential execution of multiple decision-making steps.
3Adaptability or versatility
If traditional multi-step operations are used to determine control signals, then the system can handle complex driving scenarios, but the operational efficiency is reduced
Solution Approach 1:
The patent changes the fundamental parameter of the decision-making system from a multi-step procedural approach to a direct end-to-end mapping approach using deep reinforcement learning. By training the model on diverse driving scenarios during the offline phase, it learns to handle complex situations directly through the learned policy, improving real-time efficiency without sacrificing adaptability.
Data Source
AI summary
The present disclosure relates to systems and methods for autonomous driving. The systems may obtain driving information associated with a vehicle; determine a state of the vehicle; determine one or more candidate control signals and one or more evaluation values corresponding to the one or more candidate control signals based on the driving information and the state of the vehicle by using a trained control model; select a target control signal from the one or more candidate control signals based on the one or more evaluation values; and transmit the target control signal to a control component of the vehicle.