Intersection Right-of-Way Priority Mapping for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in determining their right of way through semi-controlled or uncontrolled intersections due to the lack of clear traffic signals or signs, which can lead to safety and operational inefficiencies.
Innovation Solution
The use of map data and traffic annotation data to identify routes and priorities at intersections, with a data structure being generated and transmitted to the vehicle to determine the appropriate right of way, considering the presence or absence of traffic signals and signs, and the number of intersections with other routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use real-time observation to determine right of way at semi-controlled or uncontrolled intersections, then the vehicle can adapt to dynamic traffic conditions, but the determination process becomes complex and computationally intensive
Solution Approach 1:
The system pre-determines right of way priorities by analyzing map data, traffic annotation data, route information, and intersection characteristics before the vehicle reaches the intersection. This preliminary computation creates a lookup table or data structure that stores priority relationships between different routes, eliminating the need for complex real-time calculations when the vehicle actually approaches the intersection.
Solution Approach 2:
The patent introduces an intermediary data structure that acts as a mediator between the complex traffic rules and the vehicle control system. This data structure encapsulates the right of way priorities in a simplified format that can be quickly queried and applied, reducing the computational burden on the autonomous vehicle's decision-making system.
2Measurement precision
If autonomous vehicles perform complex real-time calculations to determine right of way, then accurate priority determination can be achieved, but computational costs and processing time increase
Solution Approach 1:
The system performs the computationally intensive analysis of traffic rules, map data, and route priorities in advance, before the vehicle reaches the intersection. The results are stored in a pre-computed data structure that can be quickly queried during real-time operation, thereby maintaining high accuracy while minimizing computational time at the critical moment of decision-making.
Solution Approach 2:
The system dynamically adjusts the level of computation based on the vehicle's proximity to the intersection. Far from the intersection, the system uses pre-computed data. As the vehicle approaches, the system selectively updates or refines specific priority determinations based on current traffic conditions, optimizing the balance between accuracy and computational effort.
3Productivity
If autonomous vehicles use simplified rules to determine right of way, then computational costs are reduced, but safety and operational efficiency may be compromised
Solution Approach 1:
The system performs comprehensive safety analysis and priority determination in advance using all available data including map information, traffic annotations, and route characteristics. This pre-computation ensures that safety-critical decisions are based on thorough analysis rather than simplified real-time rules, while the pre-computed results enable efficient execution during actual operation.
Solution Approach 2:
The system prepares contingency plans and safety margins in advance by pre-determining right of way priorities for various possible scenarios. This allows the vehicle to have pre-planned safe responses ready, reducing the need for complex real-time safety calculations while maintaining high safety standards through advance preparation.
Data Source
AI summary
Techniques for determining right of way through an intersection are discussed herein. Routes through the intersection may be associated with respective priorities. The route associated with an inbound lane devoid of yield or stop markers may be determined as being associated with the highest priority. The hierarchy of the other priorities may be organized based on whether the number of times routes associated with each respective priority intersects the route associated with the highest priority. The routes and the priorities are saved in a data structure, and the data structure is transmitted to an autonomous vehicle for controlling the autonomous vehicle through the intersection.