Autonomous Vehicle Backend Route Optimization via Mesh Network Latency Reduction
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Solution Overview
Problem
Automated or autonomous vehicles (AVs) face unacceptable transmission delays due to network latency when communicating with backend systems, particularly when managing multiple AVs, which hinders fluid operations on public roads and highways.
Innovation Solution
A backend system dynamically configures AVs' communication systems to switch between multiple channels and establish mesh networks, using network resource maps to optimize routes and connections based on latency, cost, and availability, ensuring reliable communication by prioritizing critical data and using mesh networks in areas with limited connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the backend system manages multiple AVs through centralized communication, then fleet coordination capability is improved, but network latency increases causing unacceptable transmission delays
Solution Approach 1:
The communication system is segmented into multiple channels (primary channel and secondary channels) that AVs can switch between. This segmentation allows the system to divide communication paths, reducing congestion on any single channel and thereby reducing transmission delays while maintaining fleet coordination capability.
Solution Approach 2:
Mesh networks serve as intermediaries between AVs and the backend system. When primary communication channels are unavailable or experience high latency, AVs can communicate through other AVs in the mesh network, bypassing congested direct communication paths and reducing transmission delays.
2Speed
If the system uses multiple communication channels to reduce latency, then transmission speed is improved, but system complexity increases
Solution Approach 1:
The communication system dynamically switches between primary and secondary channels based on real-time conditions such as latency measurements and channel availability. This dynamic adaptation allows the system to optimize transmission speed without requiring complex simultaneous multi-channel processing, as channels are activated selectively based on need.
Solution Approach 2:
The system changes communication parameters (channel selection, transmission power, modulation schemes) based on detected conditions. By adjusting these parameters dynamically, the system achieves variable transmission speeds without requiring a permanently complex communication architecture, simplifying the overall system design.
3Reliability
If mesh networks are established in areas with limited connectivity, then communication reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary assessments of channel availability and connectivity conditions before establishing mesh networks. By proactively identifying areas where mesh networks are needed and pre-configuring communication paths, the system avoids unnecessary mesh network formation in areas with adequate connectivity, thereby reducing energy consumption while maintaining reliability where needed.
Solution Approach 2:
AVs autonomously determine when to establish or join mesh networks based on their own communication needs and detected channel conditions. This self-service approach allows each AV to optimize its energy consumption by only engaging in mesh network communications when necessary for maintaining reliable connection to the backend system.
Data Source
AI summary
A backend system can store a network resource map that indicates network coverages areas for a plurality of base stations over a given region. The system can receive a pick-up request from a requesting user seeking transportation from a pick-up location to a destination, and instruct an automated vehicle (AV) to service the pick-up request. The system can further determine a plurality of possible routes from the pick-up location to the destination, and perform an optimization operation to determine an optimal route by utilizing the network resource map. The system can then transmit route data for the optimal route to the selected AV.