Lane Connectivity Transformer Model for Complex Intersections
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Solution Overview
Problem
Existing autonomous and semi-autonomous vehicles face challenges in accurately identifying lane connectivity, particularly at complex intersections, due to reliance on costly and error-prone sensors and inconsistencies in machine learning models.
Innovation Solution
A lane connectivity network using an autoregressive transformer model that processes images from multiple cameras around the vehicle to accurately characterize lane connectivity points, attributes, and predict future trajectories, enhancing accuracy through autoregressive blocks and attention networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If costly sensors are used for lane connectivity detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical sensor systems (LiDAR, radar, ultrasonic sensors) with a vision-based machine learning system using standard cameras. The convolutional neural network processes visual data from cameras to detect lane markings and determine connectivity, eliminating the need for costly specialized sensors while maintaining detection accuracy.
Solution Approach 2:
The system creates a virtual representation of the road layout by processing camera images through neural networks. Instead of using multiple physical sensors to directly measure lane connectivity, the system generates a digital model of the road geometry from visual data, enabling accurate lane connectivity detection through image processing rather than direct physical measurement.
2Reliability
If traditional sensors are used for object detection, then detection capability is achieved, but reliability deteriorates due to errors
Solution Approach 1:
The vision-based machine learning system performs multiple detection functions simultaneously using the same camera hardware. The neural network detects both moving objects (vehicles, pedestrians) and stationary objects (lane markings, road signs) from visual data, providing reliable detection across different object types without requiring separate specialized sensors for each function.
3Device complexity
If vision-based machine learning is used, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary processing of visual data through convolutional layers and pooling layers before final lane connectivity determination. The neural network pre-processes camera images to extract relevant features (edge detection, contour recognition, pattern identification), which improves measurement precision by preparing optimized data representations before the final detection stage.
Solution Approach 2:
The system uses loss functions and backpropagation during training to continuously improve detection accuracy. The neural network receives feedback from comparison between predicted lane connectivity and ground truth data, adjusting its internal parameters to minimize errors. This feedback mechanism enables the vision-based system to achieve high measurement precision through iterative learning and optimization.
Data Source
AI summary
Systems and methods for a vision-based machine learning model for lane connectivity in autonomous or semi-autonomous driving. An example method includes obtaining images from a multitude of image sensors positioned about a vehicle; compute forward pass-through backbone networks of a machine learning model, wherein the output of the backbone networks are fused via a transformer network; aggregating information output from the transformer network across time and/or space; and determining lane connectivity information.