Autonomous Vehicle Actuation Dynamics Latency Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional autonomous vehicle control systems neglect time-latency and actuation dynamic delays, leading to inaccuracies in following desired trajectories during rapid acceleration or sharp turns due to treating vehicle actuation latency and dynamics as negligible.
Innovation Solution
A method is developed to generate a discrete-time dynamic model characterizing actuation dynamic delay for autonomous vehicle control subsystems, using crowd-sourced driving data to estimate parameters, which are then used to determine metrics like rise time, overshoot, and settling time, enabling the creation of an updated controller that accounts for these delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If actuation latency and dynamics are treated as negligible in control system design, then device complexity is reduced, but manufacturing precision deteriorates due to visible delays in following desired trajectories
Solution Approach 1:
The system performs preliminary characterization of actuation subsystem dynamics by collecting crowd-sourced driving data and estimating dynamic parameters (mass, damping, stiffness) before controller design. This preliminary modeling allows the controller to be designed with compensation for actuation latency and dynamics, resolving the contradiction by preparing the necessary dynamic models in advance rather than treating actuation as negligible during control design
Solution Approach 2:
The system implements feedback by continuously monitoring actual actuation responses and comparing them with desired trajectories. Using the characterized dynamic parameters, the controller adjusts control commands in real-time to compensate for actuation delays and dynamics, enabling accurate trajectory following while maintaining practical controller complexity
2Measurement precision
If crowd-sourced data collection and dynamic parameter estimation are implemented, then measurement precision of actuation dynamics improves, but loss of time increases due to data processing requirements
Solution Approach 1:
Dynamic parameter estimation is performed preliminarily using historical crowd-sourced data before real-time control operations. The system collects and processes actuation data from multiple vehicles to characterize subsystem dynamics once or periodically, rather than continuously during operation. This preliminary characterization stores dynamic models that can be used directly in real-time control, resolving the time-loss contradiction by separating offline data processing from online control execution
Solution Approach 2:
The system utilizes crowd-sourced data from multiple autonomous vehicles to collectively characterize actuation dynamics. Each vehicle contributes its own actuation data to a shared dataset, allowing the system to build accurate dynamic models through aggregated real-world operation. This self-service approach leverages existing operational data without requiring dedicated testing or continuous real-time processing
3Ease of manufacture
If third-party control subsystems are used without detailed knowledge, then ease of manufacture improves, but reliability deteriorates due to uncharacterized actuation dynamics
Solution Approach 1:
The system implements feedback-based characterization by collecting actual actuation response data from third-party subsystems during normal operation. By monitoring the relationship between control commands and actual actuator responses, the system automatically identifies dynamic parameters (mass, damping, stiffness) specific to each third-party subsystem. This feedback approach maintains ease of manufacture by working with black-box third-party components while achieving reliability through data-driven dynamic modeling
Solution Approach 2:
The system adapts to third-party subsystems by estimating their specific dynamic parameters rather than assuming fixed or idealized values. The characterization process determines actual mass, damping, and stiffness parameters for each integrated subsystem, allowing the controller to be tuned to the specific characteristics of third-party components. This parameter adaptation resolves the contradiction by maintaining manufacturing simplicity while achieving control reliability through customized dynamic models
Data Source
AI summary
Systems and methods are disclosed for identifying time-latency and subsystem control actuation dynamic delay due to second order dynamics that are neglected in control systems of the prior art. Embodiments identify time-latency and subsystem control actuation delays by developing a discrete-time dynamic model having parameters and estimating the parameters using a least-squares method over selected crowd-driving data. After estimating the model parameters, the model can be used to identify dynamic actuation delay metrics such as time-latency, rise time, settling time, overshoot, bandwidth, and resonant peak of the control subsystem. Control subsystems can include steering, braking, and throttling.