Autonomous Vehicle Route Cost Modeling for Real-Time Road Conditions
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Solution Overview
Problem
Autonomous vehicles face challenges in robustly handling various road situations, and existing routing methods do not account for their specific capabilities and constraints, leading to inefficiencies and safety concerns.
Innovation Solution
A method for routing autonomous vehicles that uses a cost model incorporating various costs such as travel time, driving maneuvers, weather conditions, pedestrian traffic, and vehicle characteristics to select routes that optimize safety and efficiency, utilizing hybrid maps and real-time data for dynamic route adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing routing methods are used for autonomous vehicles, then general routing efficiency is maintained, but safety and reliability deteriorate due to inability to handle specific autonomous vehicle constraints and capabilities
Solution Approach 1:
The patent applies local quality by creating specialized cost models tailored to autonomous vehicle-specific constraints and capabilities. Different cost parameters are defined for autonomous vehicles (e.g., costs related to sensor limitations, autonomous driving zones, specific maneuver capabilities) versus traditional vehicles, allowing the routing system to optimize for AV safety and operational constraints while maintaining general routing functionality.
Solution Approach 2:
The patent implements dynamics by making the cost model adaptive and configurable based on autonomous vehicle capabilities. The system dynamically adjusts cost parameters based on the specific AV's sensor suite, autonomous driving zones, and operational constraints, allowing the routing to evolve and adapt to different AV configurations and real-time conditions.
2Productivity
If a comprehensive cost model with multiple parameters is used for autonomous vehicle routing, then routing optimization and safety improve, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the cost model into distinct, modular cost parameters. Each cost parameter (e.g., travel time, distance, autonomous vehicle-specific costs) is a separate component that can be independently calculated and combined. This modular approach allows comprehensive routing optimization while maintaining manageable system complexity through structured organization.
Solution Approach 2:
The patent implements universality by creating a cost model framework that serves multiple functions: it optimizes for traditional routing metrics (time, distance) while simultaneously accommodating autonomous vehicle-specific constraints (sensor limitations, autonomous zones, maneuver capabilities). This multi-functional cost model reduces overall system complexity by consolidating diverse requirements into a unified evaluation framework.
Data Source
AI summary
A method includes of routing an autonomous vehicle includes receiving information obtained from a camera on a second vehicle distinct from the autonomous vehicle. The method includes automatically identifying a road condition using image analysis of the information received from the camera on the second vehicle. The method includes receiving a request to route the autonomous vehicle from a first location to a second location; and in response to the request: generating a cost model for routing the autonomous vehicle, wherein the cost model includes a cost of the road condition automatically identified from the information received from the camera on the second vehicle; selecting a route from the first location to the second location in accordance with the cost model; and routing an autonomous vehicle in accordance with the selected route.