Optical Neural Navigation for Real-Time Multi-Vehicle Merging
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Solution Overview
Problem
Advanced driver-assistance systems struggle to navigate multi-vehicle conflict zones efficiently without human intervention, relying on slow and unreliable connectivity, coordination, and machine recognition systems.
Innovation Solution
A real-time machine perception system using an optical neural network (ONN) with deep learning algorithms for vehicle actuation, which processes optical signals from LiDAR sensors to generate actuation signals for collision avoidance and safe merging in conflict zones without requiring cloud, vehicle-to-vehicle, or vehicle-to-infrastructure connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional driver-assistance systems are used for navigation in multi-vehicle conflict zones, then the system can operate without specialized optical processing, but the response time is slow and human intervention is required
Solution Approach 1:
The patent replaces traditional electronic computing systems with an optical neural network that processes sensor data using optical signals and components. This substitution enables parallel processing of multiple vehicle trajectories and conflict zone scenarios, achieving real-time response without electronic computation bottlenecks
Solution Approach 2:
The optical neural network acts as an intermediary between optical sensors (LiDAR, cameras) and vehicle actuation systems. It receives optical signals directly from sensors, processes them through optical computing, and generates control signals for vehicle maneuvers, eliminating the need for electronic conversion and processing intermediate steps
2Reliability
If connectivity and coordination systems are used for vehicle navigation, then external information can be obtained, but the system becomes unreliable and impracticable in real-time scenarios
Solution Approach 1:
The vehicle system performs self-service by using its own optical sensors and onboard optical neural network to detect, analyze, and resolve conflict zone situations independently. The system processes local sensor data through optical computing to generate navigation decisions without requiring external connectivity or coordination with other vehicles
Solution Approach 2:
The patent extracts the navigation decision-making capability from external connectivity systems and embeds it within the vehicle's onboard optical processing system. The optical neural network contains pre-trained models for conflict zone navigation that operate autonomously, removing dependence on external information sources
3Measurement precision
If heavily trained machine recognition systems are used for conflict zone navigation, then accurate vehicle detection can be achieved, but the system becomes slow and data-intensive
Solution Approach 1:
The patent replaces electronic machine recognition systems with an optical neural network that performs vehicle detection and trajectory analysis using optical processing. This enables parallel computation of multiple detection features simultaneously, maintaining high accuracy while reducing processing time through the speed of light-based computation
Solution Approach 2:
The optical neural network segments the visual input from optical sensors into distinct feature components (vehicle positions, trajectories, conflict zones) and processes them through specialized optical computing pathways. This segmentation enables simultaneous processing of multiple detection tasks without electronic sequential bottlenecks
4Productivity
If optical neural networks are used for real-time processing, then fast response and autonomous navigation are achieved, but the device complexity increases
Solution Approach 1:
The patent merges the optical sensing system and optical processing system into an integrated optical end-to-end architecture. Optical sensors (LiDAR, cameras) directly feed optical signals to the optical neural network, which generates control signals for vehicle actuation, eliminating electronic conversion stages and reducing overall system complexity through functional integration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables autonomous vehicles to navigate complex conflict zones safely and efficiently by solving non-linear control problems, allowing for safe and comfortable merging without the need for external connectivity or coordination, improving response times and reducing reliance on manual intervention.
Implementation Method 1
an optical sensor for receiving a reflected portion of light from one or more objects within a field of view of the optical sensor. Optical sensor can generate an optical signal based on the reflected portion of light
Data Source
AI summary
A real-time machine perception system and vehicles with real-time perception systems can be adapted for navigation in conflict zone environments and include an optical sensor and an optical processing component coupled to the optical sensor. The optical processing component can be configured to solve a non-linear control problem comprising solving a multi-vehicle merging or multi-vehicle synchronization problem. The optical processing component can perform optical processing on a signal from the optical sensor according to a deep learning algorithm having weights determined by solving the non-linear control problem.