Fei Ye

Fei Ye, Pin Wang, Ching-Yao Chan et al.

Recent advances in supervised learning and reinforcement learning have provided new opportunities to apply related methodologies to automated driving. However, there are still challenges to achieve automated driving maneuvers in dynamically changing environments. Supervised learning algorithms such as imitation learning can generalize to new environments by training on a large amount of labeled data, however, it can be often impractical or cost-prohibitive to obtain sufficient data for each new environment. Although reinforcement learning methods can mitigate this data-dependency issue by training the agent in a trial-and-error way, they still need to re-train policies from scratch when adapting to new environments. In this paper, we thus propose a meta reinforcement learning (MRL) method to improve the agent's generalization capabilities to make automated lane-changing maneuvers at different traffic environments, which are formulated as different traffic congestion levels. Specifically, we train the model at light to moderate traffic densities and test it at a new heavy traffic density condition. We use both collision rate and success rate to quantify the safety and effectiveness of the proposed model. A benchmark model is developed based on a pretraining method, which uses the same network structure and training tasks as our proposed model for fair comparison. The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density. The collision rate is also reduced by up to 18% than the benchmark model. Finally, the proposed model shows more stable and efficient generalization capabilities adapting to the new environment, and it can achieve 100% successful rate and 0% collision rate with only a few steps of gradient updates.

Fei Ye, Xuxin Cheng, Pin Wang et al.

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane change behaviors can be a major cause of traffic flow disruptions and even crashes. While many rule-based methods have been proposed to solve lane change problems for autonomous driving, they tend to exhibit limited performance due to the uncertainty and complexity of the driving environment. Machine learning-based methods offer an alternative approach, as Deep reinforcement learning (DRL) has shown promising success in many application domains including robotic manipulation, navigation, and playing video games. However, applying DRL to autonomous driving still faces many practical challenges in terms of slow learning rates, sample inefficiency, and safety concerns. In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning, which shows great advantages in learning efficiency while still maintaining stable performance. The trained agent is able to learn a smooth, safe, and efficient driving policy to make lane-change decisions (i.e. when and how) in a challenging situation such as dense traffic scenarios. The effectiveness of the proposed policy is validated by using metrics of task success rate and collision rate. The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.

Shen Zhang, Fei Ye, Bingnan Wang et al.

Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a huge amount of unlabeled data using various sensors. In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective utilization of dataset when only a small subset of data have labels. Finally, a series of experiments is performed using both the Case Western Reserve University (CWRU) bearing dataset and the University of Cincinnati's Center for Intelligent Maintenance Systems (IMS) dataset. The experimental results demonstrate that the proposed semi-supervised learning scheme greatly outperforms two mainstream semi-supervised learning approaches and a baseline supervised convolutional neural network approach, with the overall accuracy improvement ranging between 3% to 30% using different proportions of labeled samples.