Autonomous Vehicle Route Simulation for Reliable Long-Haul Routing
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Solution Overview
Problem
Existing autonomous vehicle routing technologies fail to account for realistic conditions such as traffic, weather, and road closures, leading to inefficiencies and increased costs in long-distance trucking missions.
Innovation Solution
A method for identifying optimal routes and trajectories using simulations based on historical data and probabilistic models to predict realistic conditions, enabling efficient route planning and adaptation to changing circumstances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional routing methods are used for autonomous vehicles, then route planning is simple, but fuel efficiency and delivery reliability deteriorate due to inability to account for realistic conditions
Solution Approach 1:
The system performs preliminary simulations of multiple potential routes using historical data and probabilistic models before the actual delivery mission. By pre-evaluating routes under various realistic conditions (traffic, weather, road closures), the system identifies optimal routes in advance, ensuring reliable delivery while accounting for uncertainties before they materialize.
Solution Approach 2:
The routing system dynamically adapts to changing conditions by continuously updating simulations with real-time data and re-evaluating route options. The system adjusts route selections based on evolving traffic patterns, weather changes, and unexpected events, maintaining optimal performance throughout the delivery mission rather than relying on static pre-planned routes.
2Use of energy by moving object
If simulations based on historical data and probabilistic models are used, then route optimization and fuel efficiency improve, but computational complexity and data processing requirements increase
Solution Approach 1:
The system segments the routing problem into multiple independent simulation runs, each evaluating a different route option under various conditions. By dividing the overall optimization task into discrete simulation modules that can be executed independently and in parallel, the system achieves comprehensive route evaluation without overwhelming computational burden on a single processing unit.
Solution Approach 2:
Instead of performing complex real-time optimizations during actual delivery, the system creates virtual copies of delivery missions through simulations using historical data. These simulated copies allow the system to evaluate and compare multiple route options under realistic conditions without consuming actual fuel or requiring real-time computational resources during the actual delivery execution.
3Reliability
If multiple routes are evaluated with confidence levels, then route selection reliability improves, but time required for route planning increases
Solution Approach 1:
The system evaluates multiple routes with varying levels of detail and confidence, but does not require complete exhaustive analysis of all possible routes. By performing partial evaluations that focus on the most promising routes first and using confidence thresholds to accept satisfactory solutions, the system achieves reliable route selection without the time cost of evaluating every possible route option in exhaustive detail.
Data Source
AI summary
Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling routing of an autonomous vehicles (AV) by identifying routes from a first location to a second location, identifying a target efficiency value of autonomous driving along a respective route, determining, in view of historical data for the respective route and using one or more randomized conditions, a confidence level associated with the target efficiency value, selecting, based on the target efficiency values and the associated confidence level, a preferred route, and causing the AV to select the first route for travel from the first location to the second location.