Teleoperated Vehicle Maneuver Selection Under Network Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The performance of teleoperated driving systems is affected by the age of information in the control loop, leading to either outdated information or wireless channel congestion, and varying latencies due to processing delays and remote operator load, which can impact the safety and efficiency of remote driving maneuvers.
Innovation Solution
A system that analyzes uplink and downlink latencies, processing times, and remote operator capabilities to select optimal maneuvers based on predefined preference parameters and critical conditions, and requests assistance from other operators when necessary to ensure safe and efficient remote operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is transmitted frequently to the remote operator, then the age of information is reduced and control accuracy is improved, but wireless channel congestion occurs and system reliability deteriorates
Solution Approach 1:
The system implements periodic transmission of sensor data at optimized intervals rather than continuous transmission. The transmission frequency is dynamically adjusted based on the criticality of maneuvers and current network conditions, allowing the system to maintain up-to-date information while avoiding channel congestion that would compromise reliability.
Solution Approach 2:
The system changes the parameter of transmission frequency dynamically based on operational context. For critical maneuvers with tight latency budgets, transmission frequency increases to reduce information age. For non-critical maneuvers, frequency decreases to prevent network congestion, thereby maintaining system reliability while adapting information freshness to actual needs.
2Reliability
If sensor data is transmitted less frequently, then wireless channel congestion is reduced and system reliability is improved, but the age of information increases and control accuracy deteriorates
Solution Approach 1:
The system uses periodic transmission with dynamically adjusted intervals. By implementing periodic rather than continuous transmission, the system reduces overall network traffic and avoids congestion while maintaining acceptable information freshness through optimized transmission cycles adapted to maneuver criticality.
Solution Approach 2:
The transmission frequency parameter is changed dynamically based on maneuver criticality and latency requirements. For non-critical maneuvers, lower transmission frequency maintains system reliability by preventing network congestion. For critical maneuvers, the parameter adjusts to increase frequency, ensuring adequate information freshness without unnecessarily compromising reliability during non-critical periods.
3Loss of time
If processing frequency is increased to reduce latency, then maneuver responsiveness is improved, but processing time per maneuver decreases and accuracy may deteriorate
Solution Approach 1:
The system dynamically adjusts processing frequency based on maneuver criticality rather than using a fixed high processing rate. For critical maneuvers requiring tight latency budgets, processing frequency increases to reduce response time. For non-critical maneuvers, processing frequency decreases, allowing more computation time per maneuver and maintaining accuracy without unnecessary resource consumption.
Solution Approach 2:
The processing frequency parameter is changed dynamically according to the critical conditions of each maneuver. This allows the system to optimize the trade-off between latency and processing accuracy by allocating computational resources appropriately - high frequency processing for time-critical maneuvers, lower frequency for non-critical maneuvers where accuracy can be maintained with less urgent processing.
4Adaptability or versatility
If more maneuvers are considered in the selection process, then maneuver quality and preference matching are improved, but computational complexity increases and processing time increases
Solution Approach 1:
The system segments the maneuver selection process by evaluating maneuvers in priority order based on critical conditions and preference parameters. Rather than evaluating all possible maneuvers simultaneously with equal depth, the system segments evaluation into hierarchical levels, focusing computational resources on the most critical and preferred maneuvers first, thereby reducing overall computational complexity while maintaining selection quality.
Solution Approach 2:
The system performs preliminary filtering of maneuvers based on critical conditions and preference parameters before detailed evaluation. By pre-identifying and prioritizing candidate maneuvers that meet essential criteria, the system reduces the set of maneuvers requiring full computational evaluation, thereby maintaining adaptability and selection quality while significantly reducing computational complexity and processing time.
Data Source
AI summary
Systems and methods are provided for selecting maneuvers for teleoperation of a vehicle. The system can determine a set of maneuvers associated with a teleoperated vehicle and determine latencies between each of the set of maneuvers and the teleoperated vehicle. A preferred maneuver can be selected from the set of maneuvers. The system can determine that the preferred maneuver satisfies a critical condition based on the latency between the preferred maneuver and the teleoperated vehicle and perform the preferred maneuver using the teleoperated vehicle.