Intersection Classification Using Live Perception Without HD Maps
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Solution Overview
Problem
Conventional autonomous driving systems face challenges in accurately and efficiently detecting and classifying intersections in real-time, especially in complex urban environments, due to the reliance on detailed algorithms, separate feature detection, and the need for high-definition maps, which can be outdated or unavailable.
Innovation Solution
The system employs live perception using machine learning algorithms, such as deep neural networks, to detect and classify intersections by analyzing outputs from vehicle sensors, allowing for real-time detection and classification without prior experience or knowledge of the intersection, and reducing computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use separate feature detection and combination algorithms to detect intersections, then measurement precision of intersection features is improved, but device complexity and computational resources required increase significantly
Solution Approach 1:
The patent combines multiple separate feature detection processes (traffic lights, stop signs, lane markings, road boundaries) into a single integrated neural network model. This unified approach detects and classifies intersection features simultaneously, reducing algorithmic complexity while maintaining detection accuracy. The neural network processes sensor data holistically rather than through multiple sequential detection stages.
Solution Approach 2:
The neural network is designed as a universal detection system that handles multiple intersection feature types and various intersection configurations through a single model. Rather than requiring separate specialized algorithms for different feature types, the universal neural network adapts to detect traffic lights, signs, lanes, and boundaries within a unified framework, reducing overall system complexity.
2Measurement precision
If conventional systems execute multiple separate detection processes and combine features later, then measurement precision is improved, but productivity and real-time detection capability decrease due to high computational requirements
Solution Approach 1:
The patent merges multiple detection processes into a single neural network inference operation. By combining feature extraction, detection, and classification into one unified computational pass, the system achieves real-time performance while maintaining accurate intersection classification. This eliminates the computational overhead of executing and coordinating multiple separate detection algorithms.
Solution Approach 2:
The patent replaces the mechanical system of sequential detection and combination operations with a neural network-based computational approach. The neural network performs holistic pattern recognition and classification in a single processing stage, substituting multiple mechanical detection steps with an intelligent system that achieves the same or better precision with reduced computational complexity and faster execution.
3Reliability
If map-based solutions are used to interpolate intersections, then reliability in areas with available maps is improved, but adaptability to areas without maps or with transient conditions deteriorates
Solution Approach 1:
The neural network-based system provides self-service detection capabilities, independently identifying and classifying intersections without relying on external map data. The system processes sensor data directly to detect intersection features and make navigation decisions, making it adaptable to any environment including areas without HD maps or with transient conditions not reflected in static map data.
Data Source
AI summary
In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.