Real-Time Fail-Operational Controller for Autonomous Driving in Dynamic Environments

Conceptos Básicos

The author presents a fail-operational controller using incremental Bayesian learning to ensure safety and efficiency in autonomous driving under environmental disturbances.

Resumen

The paper introduces a fail-operational controller for autonomous vehicles to handle abrupt maneuvers by surrounding vehicles. It employs incremental Bayesian learning to adapt to changing environmental disturbances while ensuring safety and efficiency. The proposed framework is validated in connected cruise control tasks, demonstrating effective real-time safety recovery and task efficiency even under time-varying disturbances.
Key points:

Safety concerns from model uncertainties due to environmental disturbances.
Real-time fail-operational controller ensures asymptotic convergence of the EV to a safe state.
Incremental Bayesian learning approach facilitates online adaptation to changing disturbances.
Stochastic fail-operational barrier integrated into an efficient controller based on quadratic programming.
Validation in connected cruise control tasks shows swift return to safe state with maintained task efficiency.

Estadísticas

"Our simulation results on a CCC task have substantiated the efficacy of our fail-operational controller, showcasing its ability to safely guide an unsafe EV to a safe state while sustaining the desired performance."
"The average solving times are 2.349 ms and 3.035 ms for initial states x0 = [25 m, 18 m/s]T and x0 = [110 m, 18 m/s]T, respectively."
"Learning and inference time remain consistently low, with averages of less than 5 ms and 0.4 ms, respectively."

Citas

"Our method empowers the EV to swiftly return to a safe state while upholding task efficiency in real time."
"The proposed framework is validated in connected cruise control tasks, demonstrating effective online learning and safety recovery for the EV."

Ideas clave extraídas de

Incremental Bayesian Learning for Fail-Operational Control in Autonomous Driving

by Lei Zheng,Ru... a las arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04143.pdf

Consultas más profundas

How can this fail-operational controller be adapted for different types of autonomous vehicles?

The fail-operational controller described in the context can be adapted for various types of autonomous vehicles by customizing the control barrier functions (CBFs) and exponentially stabilizing control Lyapunov functions (ES-CLFs) based on the specific dynamics and constraints of each vehicle. Different vehicles may have unique mass, aerodynamic properties, control input ranges, and desired performance metrics. By adjusting these parameters in the controller design, it can cater to diverse autonomous vehicles such as cars, drones, or even marine vessels.

What are the potential ethical implications of relying on real-time controllers for autonomous driving systems?

Relying on real-time controllers for autonomous driving systems raises several ethical considerations. One major concern is ensuring safety and minimizing risks to passengers, pedestrians, and other road users. Real-time decisions made by controllers must prioritize human life over property or efficiency concerns. Additionally, there are issues related to accountability and liability in case of accidents or failures where human intervention might not be possible quickly enough. Privacy concerns also arise due to data collection requirements for real-time decision-making processes.

How might advancements in incremental Bayesian learning impact other fields beyond autonomous driving?

Advancements in incremental Bayesian learning have far-reaching implications beyond autonomous driving:

Healthcare: Personalized treatment plans could benefit from adaptive learning models that continuously update based on patient data.
Finance: Risk assessment models could become more accurate with real-time updates using Bayesian techniques.
Manufacturing: Predictive maintenance schedules could improve through continuous monitoring and updating based on equipment performance data.
Climate Science: Environmental modeling could benefit from adaptive learning algorithms that adjust predictions based on changing climate patterns.
Robotics: Autonomous robots could enhance their decision-making capabilities by incorporating incremental Bayesian learning methods into their navigation systems.

These advancements have the potential to revolutionize various industries by enabling dynamic adaptation to evolving conditions while maintaining high levels of accuracy and efficiency across a wide range of applications beyond just autonomous driving systems.