Connected Vehicle Network Optimization via Virtual GIS Overlay
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Solution Overview
Problem
Connected vehicles face challenges in achieving optimal network performance due to the abundance of wireless network operators and the proliferation of low-power base stations with small overlapping areas, which existing techniques struggle to satisfy, especially in terms of quality of service for autonomous and teleoperated vehicles.
Innovation Solution
A method that involves a vehicle establishing multiple network connections, predicting its future state, and dynamically adjusting network parameters based on sensed information and performance data to optimize communication, including configuring the communication system to transmit data streams redundantly across different networks to ensure low latency and high throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple network connections are established and dynamically adjusted, then network performance and quality of service are improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting future vehicle states (position, speed, acceleration) and pre-determining optimal network parameters before the vehicle actually reaches those states. This allows the communication system to proactively configure network connections in advance, improving responsiveness and quality of service without requiring complex real-time decision-making mechanisms.
Solution Approach 2:
The system dynamically adjusts network parameters (bandwidth, latency requirements, connection priorities) based on predicted future vehicle states and current network conditions. Multiple network connections are maintained concurrently with dynamically changing parameters, allowing the system to adapt to varying quality of service requirements as the vehicle moves through different geographic areas with different network coverage characteristics.
2Loss of time
If network parameters are dynamically adjusted based on predicted future states, then latency is reduced and quality of service is optimized, but computational requirements and processing time increase
Solution Approach 1:
The system computes optimal network parameters in advance by predicting future vehicle states and determining the network configuration that will be needed before the vehicle reaches the relevant geographic area. This preliminary computation reduces the need for complex real-time calculations, thereby reducing latency while managing computational power requirements through proactive rather than reactive processing.
3Reliability
If redundant data transmission over multiple networks is implemented, then reliability and fault tolerance are improved, but network bandwidth consumption and data usage increase
Solution Approach 1:
The system implements partial redundancy by transmitting data over multiple network connections with dynamically adjusted parameters. Rather than duplicating all data across all networks, the system optimizes the distribution of data traffic across available networks based on predicted performance, using just enough redundancy to achieve the desired quality of service and reliability targets while minimizing unnecessary data consumption.
Data Source
AI summary
In a connected vehicle environment, network connection parameters such as a network congestion window and bit rate are automatically adjusted dependent on a location of a vehicle in order to optimize network performance. A geospatial database stores learned relationships between network performance of a connected vehicle at different physical locations when configured in accordance with different network parameters. The vehicle can then adjust its network parameters dynamically dependent on its location. A vehicle may maintain multiple connections to different networks concurrently for transmitting duplicate data of a data stream, with the vehicle independently adjusting parameters associated with different networks to optimize performance.