Traffic Pattern Connectivity Models for Autonomous Lane Rule Inference
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Solution Overview
Problem
Autonomous vehicles face challenges in understanding lane connectivity and traffic rules in complex road environments, as they lack the ability to interpret human-driven traffic patterns and local driving etiquette.
Innovation Solution
A connectivity model is trained using vehicle traffic-pattern data to learn lane relationships and traffic rules, incorporating sensor data from various sources to infer geometric areas and yield relationships, which are then encoded into a semantic map for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use traditional traffic rule encoding methods, then the system structure is simple, but the vehicle cannot understand complex lane connectivity and local driving etiquette
Solution Approach 1:
The system performs preliminary learning by training a connectivity model on historical traffic pattern data before actual navigation. This pre-training phase enables the vehicle to understand complex lane connectivity and traffic rules in advance, resolving the contradiction between simple structure and high adaptability.
Solution Approach 2:
The system copies human driving behavior patterns by training the connectivity model on traffic pattern data collected from human-driven vehicles. This copying approach allows the autonomous vehicle to learn implicit traffic rules and lane connectivity without explicit programming, achieving high adaptability while maintaining reasonable system complexity.
2Measurement precision
If the connectivity model is trained on extensive traffic pattern data, then the understanding of traffic rules improves, but the training time and computational resources increase
Solution Approach 1:
The system performs model training as a preliminary action before deployment, allowing extensive data processing to occur offline. This separates the time-consuming training phase from the real-time navigation phase, achieving high measurement precision without sacrificing operational time.
Solution Approach 2:
The system uses partial action by training on a representative subset of traffic pattern data that captures essential driving behaviors. This approach achieves sufficient accuracy for practical navigation while reducing the total training time and computational resources required.
3Measurement precision
If the system infers yield relationships and geometric areas from sensor data, then the navigation accuracy improves, but the processing complexity increases
Solution Approach 1:
The connectivity model serves as an intermediary that processes raw sensor data and historical traffic patterns to infer yield relationships and geometric areas. This intermediary layer simplifies the processing complexity by consolidating inference logic in a dedicated model rather than distributing it across multiple system components.
Solution Approach 2:
The system copies the human cognitive process of understanding spatial relationships and right-of-way rules by training the connectivity model on observed traffic patterns. This allows the system to infer complex navigation parameters from relatively simple sensor inputs, achieving high navigation accuracy without proportionally increasing processing complexity.
Data Source
AI summary
In one embodiment, a method includes determining a connectivity model associated with a region of a road network, wherein the connectivity model was trained using vehicle traffic-pattern data comprising a first lane identifier and a second lane identifier indicating one or more lanes associated with a vehicle trajectory through the region and a traffic-light state corresponding to signal information of traffic lights in the region when a vehicle moved through the region, determining for at least one egress lane in the region based on the connectivity model a lane relationship indicating one or more ingress lanes in the region onto which a vehicle in the egress lane can move and one or more governing traffic lights selected from the traffic lights in the region that govern the egress lane, and encoding the lane relationship and the one or more governing traffic lights into a map of the region.