Vehicle Navigation Under Sensor and Map Uncertainty
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safely and accurately due to the need to process and interpret various sensory data, identify obstacles, and make real-time navigational decisions based on visual information and environmental conditions.
Innovation Solution
A navigational system using multiple cameras and sensors to analyze overlapping environmental data, identify target objects, and adjust vehicle actuators based on detected driving conditions to trigger or forego navigational adjustments, employing reinforcement learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and cameras are used to capture environmental data, then the reliability of obstacle detection is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensors (cameras, radar, lidar) and their data streams into a unified sensing system that processes environmental information collectively. This merging approach improves obstacle detection reliability by cross-validating detections across multiple sensor types while managing complexity through integrated processing architecture.
Solution Approach 2:
The patent introduces reinforcement learning agents as intermediary components that mediate between raw sensor data and navigation decisions. These agents process and interpret sensor outputs, resolving the complexity of integrating multiple sensor types by providing a standardized interface for decision-making.
2Adaptability or versatility
If reinforcement learning techniques are employed for real-time navigational decisions, then the adaptability of the vehicle is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent pre-trains reinforcement learning agents using simulated environments and historical data before deployment. This preliminary training allows the agents to develop decision-making capabilities in advance, reducing the computational burden and processing time required for real-time navigational decisions during actual vehicle operation.
Solution Approach 2:
The patent implements dynamic adjustment of reinforcement learning agent behavior based on operational context. The system adapts its decision-making complexity in real-time, using pre-trained models for routine situations and engaging more intensive processing only when novel or hazardous conditions are detected, thereby balancing adaptability with processing time constraints.
Data Source
AI summary
The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may a first output from a first sensor and a second output from a mapping system; identify a target object in the first output; and determine, based on the first output, a detected driving condition associated with the target object and whether the condition triggers a navigational constraint. If the navigational constraint is triggered, the system may cause a first navigational adjustment. If the navigational constraint is not triggered, the system may determine whether a representation of the target object is included in the second output. If the representation of the target object is included in the second output, the system may cause a second navigational adjustment. If the representation of the target object is not included in the second output, the system may forego any navigational adjustments.