Autonomous Vehicle Routing Using Flexibility Metrics for Timed Arrival
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Solution Overview
Problem
Autonomous vehicles often arrive at their destinations too early, leading to idle time, increased fuel consumption, security risks, and inefficient use of vehicle components due to the reliance on shortest path optimization, which neglects other driving parameters and user productivity considerations.
Innovation Solution
A method for routing autonomous vehicles that considers flexibility metrics based on travel deviation times of road segments, allowing the vehicle to adjust speed and arrive within a target time interval, minimizing idle time and optimizing for fuel consumption and component wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the route is planned to arrive as quickly as possible using shortest path optimization, then the travel time is reduced, but the autonomous vehicle arrives too early and must wait idle at the destination
Solution Approach 1:
The patent changes the optimization parameter from minimizing travel time to minimizing total time including idle time. The routing system calculates arrival times and compares them against destination availability time slots, selecting routes that achieve timely arrival without excessive waiting, thereby improving vehicle productivity while maintaining reasonable travel duration.
2Loss of time
If the autonomous vehicle arrives too early at the destination, then the travel time is reduced, but the vehicle becomes vulnerable to break-ins and theft during idle waiting time
Solution Approach 1:
The system performs preliminary calculation of arrival times and proactively selects routes that ensure arrival within acceptable time windows. By predicting arrival times before routing decisions are finalized, the system prevents early arrivals that would create security vulnerabilities, ensuring the vehicle arrives close to the target time without prolonged idle waiting at the destination.
3Loss of time
If the autonomous vehicle arrives too early at the destination, then the travel time is reduced, but fuel consumption and wear of vehicle components increase due to idle time
Solution Approach 1:
The optimization criterion changes from minimizing only travel time to minimizing a composite metric that includes travel time plus idle time penalties. This parameter change causes the routing algorithm to select routes with more moderate travel durations that avoid excessive waiting, thereby reducing overall energy consumption and component wear while still achieving timely delivery.
4Loss of time
If the autonomous vehicle arrives too early at the destination, then the travel time is reduced, but the user must wake up during night-time to perform tasks, which is bad for productivity and health
Solution Approach 1:
The system incorporates feedback about user availability and destination operating hours into the routing decision-making process. By comparing predicted arrival times against known time slots when destinations are accessible and users are available, the routing algorithm adjusts route selection to ensure arrivals occur during productive daytime hours rather than early morning or nighttime, thereby maintaining user productivity and health.
Data Source
AI summary
A method for routing an autonomous vehicle from a start location to a target destination location via a set of road segments is provided. The method includes determining at least two candidate routes comprising a respective subset of the set of road segments. Each of the at least two candidate routes indicates how the autonomous vehicle shall travel from the start location, to arrive at the target destination location within a time interval of a target time of arrival. The method determines a flexibility metric for each of the at least two candidate routes indicative of a possible time adjustment when adjusting a speed of the autonomous vehicle. The method selects a route based on the determined flexibility metrics.