Autonomous Terminal Tractor Fleet Control With Real-Time Map Updates
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Solution Overview
Problem
Autonomous vehicle operations face challenges in optimizing duty cycles and route planning due to intermittent maintenance, refueling, and recharging requirements, which disrupt their working activities and require outdated map data for navigation.
Innovation Solution
A remote computing system that selects vehicles based on their status and optimizes duty cycles by transmitting centralized map data in real-time, allowing vehicles to autonomously transport loads while collecting sensor data and updating the map, ensuring the most up-to-date navigation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate continuously without interruption, then productivity is improved, but maintenance and refueling requirements cause downtime that reduces productivity
Solution Approach 1:
The system performs preliminary actions by predicting maintenance needs and refueling requirements before they occur. The fleet management system monitors vehicle status in real-time and schedules maintenance and refueling activities in advance, optimizing the duty cycle to minimize interruptions to working activities while ensuring vehicles are serviced before critical failures occur.
2Measurement precision
If centralized map data is updated continuously in real-time, then navigation accuracy is improved, but data transmission and processing complexity increases
Solution Approach 1:
The system merges map data collection and updating functions across the entire fleet into a centralized system. Multiple vehicles contribute sensor data simultaneously, and the fleet management system consolidates this information to create and update a single authoritative map dataset. This approach achieves high measurement precision through aggregated data while managing complexity through centralized processing rather than requiring each vehicle to maintain independent updated maps.
3Loss of information
If vehicles autonomously collect and update map data during transport, then map data currency is improved, but vehicle computing resource consumption increases
Solution Approach 1:
Vehicles autonomously collect sensor data during their normal transport operations without requiring dedicated map updating missions. The system leverages the vehicles' existing movement and sensing capabilities to gather map information as a byproduct of their primary function. This self-service approach maintains current map data while minimizing additional energy consumption, as the data collection occurs during regular working activities rather than requiring separate resource-intensive updating operations.
Data Source
AI summary
Systems and methods for autonomous vehicle control are provided. A remote computing system includes a communication interface coupled to a network, a map database storing centralized map data, a processor, and a memory storing instructions that, when executed by the processor, cause the processor to perform operations. The operations include: receiving a transport request including a first location and a second location; selecting a first vehicle of a plurality of vehicles for the transport request based on at least one of the transport request and a vehicle status; transmitting the centralized map data to the first vehicle; causing the first vehicle to complete the transport request, including causing the first vehicle to autonomously transport from the first location to the second location and causing the first vehicle to collect first sensor data; receiving the first sensor data in real-time; and, updating the centralized map data with the first sensor data.