Neural Lane Graph Generation From Multi-Sensor Vehicle Data
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Solution Overview
Problem
Conventional lane detection methods for autonomous vehicles are limited by inaccuracies and inconsistencies due to reliance on single sensor data types and online processing, leading to ineffective navigation in complex environments.
Innovation Solution
Utilizing a deep neural network (DNN) with transformer architecture to infer lane data from sensor data collected by a fleet of ego-machines, generating lane graphs that model the environment for localization and navigation, leveraging polyline and point features from LiDAR and RADAR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lane detection methods use single sensor data types and online processing, then device complexity is reduced, but lane detection accuracy and reliability deteriorate
Solution Approach 1:
The patent combines multiple sensor data types (LiDAR, RADAR, cameras) to create a comprehensive input for the neural network. This multi-sensor fusion approach improves lane detection accuracy by compensating for the limitations of individual sensors and providing redundant information for more reliable detection.
Solution Approach 2:
The system performs offline processing to pre-process and store sensor data before actual lane detection is needed. This preliminary action allows the system to build a rich dataset and train models in advance, improving online detection accuracy without increasing real-time computational complexity.
2Measurement precision
If offline processing is used to enhance lane detection accuracy, then computing resource usage during operation is reduced, but processing time before deployment increases
Solution Approach 1:
The system performs computationally intensive data processing, sensor fusion, and neural network training offline before deployment. This preliminary action shifts the computational burden to a pre-processing phase, allowing accurate lane detection models to be deployed with minimal real-time computing requirements.
Solution Approach 2:
The processing workflow is segmented into distinct phases: offline data collection and pre-processing, model training, and online inference. This segmentation allows the system to perform heavy computational tasks during offline phases while maintaining low-latency performance during actual lane detection operations.
3Reliability
If trajectory-based methods are used to identify lanes, then device complexity is reduced, but measurement precision and reliability deteriorate due to inconclusive information
Solution Approach 1:
The patent merges trajectory information with direct sensor observations (LiDAR points, RADAR detections, camera images) as inputs to the neural network. This combination allows the system to leverage the simplicity of trajectory-based approaches while compensating for their reliability limitations through multi-sensor validation and fusion.
Data Source
AI summary
In various examples, various types of sensor data from multiple ego-machines are used to infer lanes and/or generate lane graphs for use in autonomous systems and applications. In some embodiments, one or more DNNs may be used to infer lane data indicating a representation of a lane shape using sensor data from various vehicles to represent a 3D environment. The inferred lane data may include cross-section indicators that indicate cross-sections of a lane and/or connection indicators that indicate a lane channel connecting two locations (e.g., two lane portions). The inferred lane data may be used to generate a lane graph that represents lanes on a road and, in some cases, lane dividers (e.g., polyline represented as a solid line, a dashed line, a double line, etc.). A lane graph may be used, for example, to model the environment around a vehicle, facilitate localization, provide guidance for autonomous driving, etc.