Learning-Based Modeling of Human Driving Behavior to Enhance Safety in Mixed-Vehicle Platooning

Core Concepts

A hybrid model combining a first-principles approach and a Gaussian process learning component improves the accuracy of velocity predictions for human-driven vehicles and provides measurable uncertainty estimates, enabling the development of a safer model predictive control strategy for mixed-vehicle platooning.

Abstract

The paper introduces a novel method for modeling the interactions between human-driven vehicles (HVs) and autonomous vehicle (AV) platoons in longitudinal car-following scenarios. The proposed approach combines a traditional first-principles model with a Gaussian process (GP) learning component to enhance the accuracy of velocity predictions for HVs and provide measurable uncertainty estimates.
The key highlights are:

The hybrid HV model, consisting of an ARX nominal model and a GP-based correction, reduces the modeling error by 35.6% compared to the first-principles model alone.
A chance-constrained model predictive control (MPC) strategy, GP-MPC, is developed that incorporates the proposed HV model to estimate modeling uncertainties as an additional probabilistic constraint, enhancing safety.
Simulation experiments demonstrate that the GP-MPC ensures a greater safety margin between vehicles and allows for higher vehicle speeds, improving the efficiency of the mixed platoon, compared to a traditional baseline MPC.
The computational complexity of the GP-MPC is significantly reduced by employing a sparse GP technique and integrating dynamic GP prediction within the MPC, resulting in an average computation time that is only 5% longer than the baseline MPC and approximately 100 times faster than previous models.

Stats

The average RMSE of the ARX model was 1.88 m/s, while the ARX+GP model achieved an average RMSE of 1.21 m/s, indicating a 35.64% improvement in modeling accuracy.
The sparse GP+ARX model had an average RMSE of 1.43 m/s, a 23.94% improvement over the ARX model.
The GP-MPC achieved a minimum HV-AV distance of 22.27 m, which is 2 meters more than the nominal MPC.
The average computation time of the GP-MPC is only 5% more than the nominal MPC.

Quotes

"The proposed modeling approach's effectiveness for HVs is further demonstrated through a model predictive control strategy designed specifically to utilize this model."
"The GP-MPC attained a minimum HV–AV distance of 22.27 m, which is two meters more than the nominal MPC. This enhancement is attributed to the incorporation of an additional component in the safe distance constraint that accounts for the GP uncertainty assessments."
"Compared to our previous work (Wang et al., 2024) that did not employ sparse GP modeling for HVs and the dynamic GP prediction technique in MPC, the average computation time per time step for the GP-MPC has been reduced by approximately 100 times."

Key Insights Distilled From

Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control

by Jie Wang,Yas... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2303.09452.pdf

Deeper Inquiries

How can the proposed approach be extended to handle more complex traffic scenarios, such as human-driven vehicles within the platoon or involving merging and lane-changing maneuvers?

The proposed approach can be extended to handle more complex traffic scenarios by incorporating additional dynamics and interactions into the modeling and control framework. To address scenarios with human-driven vehicles within the platoon, the modeling of human behavior can be further refined to account for the variability and unpredictability of human drivers. This can involve collecting data on diverse driving styles and behaviors to enhance the GP model's ability to predict human responses accurately. Additionally, the control strategy can be adapted to consider the interactions between multiple human-driven vehicles and autonomous vehicles within the platoon.
For scenarios involving merging and lane-changing maneuvers, the modeling approach can be expanded to include additional state variables and constraints related to these specific maneuvers. The GP model can be trained on data that captures a wide range of merging and lane-changing behaviors to improve its predictive capabilities in these situations. The control strategy can then be designed to incorporate these specific maneuvers, ensuring safe and efficient interactions between vehicles during merging and lane-changing actions.

How can the diversity and representativeness of the training data for the GP models be improved to enhance the generalization capabilities of the proposed approach?

To enhance the diversity and representativeness of the training data for the GP models, several strategies can be employed:

Data Collection: Collect data from a wide range of driving scenarios, environments, and driver behaviors to ensure a comprehensive dataset that captures the variability in human driving.

Data Augmentation: Use techniques such as data augmentation to create synthetic data points that represent different driving conditions and behaviors. This can help in expanding the dataset and improving the model's generalization capabilities.

Balanced Dataset: Ensure a balanced dataset that includes equal representation of different driving scenarios, road conditions, and driver characteristics. This can prevent bias in the model and improve its ability to generalize to unseen data.

Cross-Validation: Implement cross-validation techniques to validate the model's performance on different subsets of the data. This can help in assessing the model's generalization capabilities and identifying areas for improvement.

By implementing these strategies, the diversity and representativeness of the training data for the GP models can be enhanced, leading to improved generalization capabilities and more robust performance in a variety of traffic scenarios.

How can the proposed approach be extended to handle more complex traffic scenarios, such as human-driven vehicles within the platoon or involving merging and lane-changing maneuvers?

The proposed approach can be extended to handle more complex traffic scenarios by incorporating additional dynamics and interactions into the modeling and control framework. To address scenarios with human-driven vehicles within the platoon, the modeling of human behavior can be further refined to account for the variability and unpredictability of human drivers. This can involve collecting data on diverse driving styles and behaviors to enhance the GP model's ability to predict human responses accurately. Additionally, the control strategy can be adapted to consider the interactions between multiple human-driven vehicles and autonomous vehicles within the platoon.
For scenarios involving merging and lane-changing maneuvers, the modeling approach can be expanded to include additional state variables and constraints related to these specific maneuvers. The GP model can be trained on data that captures a wide range of merging and lane-changing behaviors to improve its predictive capabilities in these situations. The control strategy can then be designed to incorporate these specific maneuvers, ensuring safe and efficient interactions between vehicles during merging and lane-changing actions.