Intersection Contention Classification Using Signed Distance Functions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional autonomous driving systems face challenges in accurately detecting and classifying intersection contention areas, especially in complex urban environments, due to reliance on detailed annotations for deep neural networks and the failure to handle occlusions and transient conditions without up-to-date HD maps.
Innovation Solution
A deep neural network-based system processes sensor data to detect and classify intersection contention areas in real-time, using signed distance functions and post-processing techniques to determine 3D world-space locations, allowing vehicles to navigate intersections safely without prior knowledge or detailed maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use multiple separate deep neural networks to detect and combine various features (traffic lights, stop signs, vehicle positions, lanes, etc.) for intersection detection, then the detection accuracy may be improved, but the device complexity and computational intensity increase significantly
Solution Approach 1:
The patent combines multiple separate deep neural networks that detect different features (traffic lights, stop signs, vehicle positions, lanes, free-space boundaries) into a single integrated neural network. This unified network processes sensor data and simultaneously outputs all necessary features for intersection detection and classification, thereby reducing system complexity while maintaining detection accuracy.
2Measurement precision
If detailed annotations are required to train deep neural networks for complex intersection detection, then the measurement precision improves, but the ease of manufacture and scalability deteriorate
Solution Approach 1:
The patent creates a universal neural network model that can handle multiple intersection types and scenarios through a single trained model. The network is designed to be multi-functional, detecting and classifying various intersection configurations (crossroads, T-intersections, roundabouts, etc.) without requiring separate specialized networks for each intersection type, thereby improving scalability while maintaining accuracy.
3Reliability
If map-based solutions are used to compare detected features with pre-stored high-definition maps, then the reliability of intersection understanding improves, but the system fails when maps are outdated or unavailable
Solution Approach 1:
The patent implements a self-service detection system where the neural network autonomously detects and classifies intersections using only sensor data from the vehicle's own sensors (cameras, LIDAR, RADAR). The system does not rely on external HD maps or pre-stored geographic information, allowing it to independently understand and adapt to any intersection configuration it encounters, including transient conditions like police-directed traffic or stopped school buses.
4Loss of information
If conventional systems individually detect and combine multiple features to understand intersections, then the completeness of intersection understanding may be improved, but the loss of time and computational resources increase
Solution Approach 1:
The patent implements continuous real-time detection and classification of intersections using the neural network. The system processes sensor data continuously as the vehicle moves, providing ongoing updates of intersection status, classification, and relevant features without interruption. This continuous processing ensures complete intersection understanding while optimizing processing time through the efficient single-network architecture.
Data Source
AI summary
In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas 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 outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.