Neural Network Action Model for Autonomous Vehicle State Prediction
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Solution Overview
Problem
Current vehicle dynamics models, such as the bicycle model, are limited in accuracy as they only consider the vehicle's state and not its surrounding environment, and deep reinforcement learning algorithms like DQN require extensive data and training time, leading to inefficient autonomous driving systems.
Innovation Solution
A neural network-based action model is developed to predict the subsequent state of a vehicle in its environment, using sensory data like images, LIDAR, and RADAR, which learns from simulation or real-world data to improve accuracy and reduce training time, enabling faster and more reliable autonomous driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning algorithms like DQN are used to learn autonomous driving policies, then the system can achieve adaptive decision-making, but the training time and data requirements become excessively large
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network offline using extensive simulation data and real-world data collected beforehand. This pre-training phase prepares the network with prior knowledge of driving scenarios, allowing the system to achieve adaptive decision-making without requiring extensive real-time training. The network learns from a diverse dataset including various weather conditions, road types, and traffic situations in advance, so that during actual autonomous driving operation, it can make adaptive decisions immediately without prolonged training periods.
2Ease of manufacture
If traditional bicycle models are used for vehicle dynamics, then the system is simple to implement, but the prediction accuracy in various driving scenarios is insufficient
Solution Approach 1:
The patent replaces the traditional mechanical bicycle model with a neural network-based predictive model. Instead of relying on fixed mathematical equations that assume ideal conditions, the neural network learns complex vehicle dynamics patterns from data, capturing non-linear effects such as tire saturation, aerodynamic drag, and suspension behavior. This substitution maintains computational efficiency while dramatically improving prediction accuracy across diverse driving scenarios including slippery roads, steep gradients, and high-speed maneuvers where traditional models fail.
3Device complexity
If vehicle dynamics models only consider the vehicle's state without environmental factors, then the model remains simple, but the accuracy in various driving scenarios deteriorates
Solution Approach 1:
The patent merges the vehicle state model with environmental perception data by integrating inputs from multiple sensors including cameras, LIDAR, RADAR, and GPS into a unified neural network architecture. The network processes both the vehicle's dynamic state (position, velocity, acceleration) and environmental context (road conditions, weather, surrounding vehicles, terrain) simultaneously. This combination allows the model to adapt predictions based on environmental factors such as icy roads reducing traction, wind affecting vehicle stability, or uphill gradients impacting acceleration, thereby significantly improving prediction accuracy without excessive complexity increase.
Data Source
AI summary
A method, device and system of prediction of a state of an object in the environment using a pre-trained action model defined by an action model neural network. A control system for an object comprises a plurality of sensors for sensing a current state and an environment in which the object is located, and a first neural network. Predicted subsequent states of the object in the environment are obtained using the action model and a current state of the object in the environment The action model maps a plurality of state-action pairs (s, a), each state-action pair encoding a state (s) of the object in the environment and an action (a) performed by the object to a predicted subsequent state (s′) of the object in the environment. An action that maximizes a value of a target, based at least on a reward for each of the predicted subsequent states, is determined. The determined action is caused to be performed.