Neural-Network Driver Assistance for Continuous Obstacle-Aware Pathing
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Solution Overview
Problem
Conventional driver-assistance systems face challenges in predicting vehicle paths due to the presence of static and dynamic objects on the road, as they require precise target positions and stepwise trajectories, which can be laborious and inexact.
Innovation Solution
A deep neural network is used to create a continuous mathematical function representing a path for the vehicle, which is updated regularly, allowing the vehicle to circumnavigate obstacles optimally without relying on target positions, utilizing a grid populated with object detection information and incorporating convolutional layers and polynomial coefficients for path prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trajectory calculation methods are used that rely on known target positions, then the path prediction can be calculated stepwise, but the process becomes laborious and inexact when objects are merely detected without target position information
Solution Approach 1:
The patent replaces conventional mechanical trajectory calculation methods with a neural network-based system. Instead of using stepwise trajectory calculations that require known target positions, the neural network directly processes detected objects and their attributes to predict the vehicle path in a continuous manner, eliminating the need for manual target position determination and reducing calculation complexity
Solution Approach 2:
The patent changes the input parameters from requiring known target positions to using only detected object attributes (type, position, velocity, acceleration). The neural network learns to map these parameters directly to path predictions, transforming the problem from a constraint-based calculation to a data-driven prediction that works with available sensor data
2Adaptability or versatility
If stepwise trajectories with multiple target positions are calculated to account for different object behaviors, then the path prediction can handle static and dynamic objects, but the calculation becomes more laborious and requires several target positions
Solution Approach 1:
The patent creates a universal neural network model that handles both static and dynamic objects through a single integrated architecture. The network processes all detected objects uniformly, using their attributes (type, position, velocity, acceleration) to automatically adapt the path prediction, eliminating the need for separate trajectory calculations for different object types
Solution Approach 2:
The patent uses a grid-based representation that copies the spatial environment into a discrete format, where each cell contains information about objects and their attributes. This grid representation serves as a simplified model that the neural network processes to predict the continuous path, replacing complex multi-target trajectory calculations with a unified grid-to-path mapping
3Productivity
If conventional systems require target positions for trajectory calculation, then the path can be predicted stepwise, but manual determination of target positions is laborious and inexact
Solution Approach 1:
The neural network system is self-sufficient in determining the path without requiring external input of target positions. It automatically processes detected object data, learns the mapping from object attributes to path predictions during training, and generates continuous path predictions independently, eliminating manual target position determination entirely
Data Source
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AI summary
The invention relates to a driver-assistance system (100) for a vehicle (110) comprising a processing unit (102) configured to receive information about objects on a road, map the objects (120, 122) into a grid, create a mathematical function based on the grid, wherein the mathematical function represents a path (402) for the vehicle (110) on the road, and guide the vehicle (110) along the path (402).