Recent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated the research and applications of deep learning-based traffic object recognition from Light Detection and Ranging (LiDAR) data. The majority of existing methodologies applied deep learning (DL)-based techniques, especially Convolutional Neural Networks (CNNs), for vehicle detection and tracking on autonomous driving datasets. Nevertheless, fewer studies were focused on DL-based vehicle detection using roadside LiDAR data, partially due to the lack of publicly available roadside LiDAR datasets for network training and testing. This paper develops a novel framework based on CNNs and LiDAR data for automated vehicle detection. It leverages the domain knowledge of CNNs trained on large-scale autonomous driving datasets for vehicle detection from roadside LiDAR data. In the experimental study, roadside LiDAR data were collected at a road intersection in Reno, Nevada, U.S. Meanwhile, a CNN architecture was proposed to detect vehicles from LiDAR data through 3D bounding boxes. The proposed CNN was modified from the established PointPillars network by adding dense connections to the convolutional layers to achieve more comprehensive feature extraction. Three CNNs, including the proposed CNN, PointPillars, and YOLOv4, were trained and tested on PandaSet, a publicly available large-scale autonomous driving LiDAR dataset. Subsequently, the trained CNNs were reused for vehicle detection from the captured roadside LiDAR data. The experimental results demonstrated that the proposed CNN outperformed the others in the testing metrics. All three networks showed good performance on vehicle detection from the captured roadside LiDAR data.

Recent studies have demonstrated the benefits of water-dielectric interfaces in electrostatic energy harvesting. Most efforts have been focused on extracting the kinetic energy from the motions of water drops on hydrophobic surfaces, and thus, the resulting schemes inherently prefer cases where the water drops move at a high speed, or vibrate at a high frequency. Here we report a method for directly harvesting ambient mechanical energy as electric potential energy through water droplets by making alternate contacts with CYTOP and PTFE thin films. Because CYTOP and PTFE acquire significantly different surface charge densities during contact with water, such a difference can be utilized to effectively generate electricity. We demonstrate this concept using prototype devices fabricated on silicon substrates with a simple procedure. In the experiments conducted, a water drop of 400 μL alone could generate a peak open-circuit voltage of 42 V under a 0.25 Hz vibration. Under a 2.5 Hz vibration, the peak open-circuit voltage reached 115 V under an external bias of 8 V. The demonstrated efficiency is orders of magnitude higher than those of existing devices of similar dimensions.

This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation. These ML models tend to be very complex and non-linear in nature, making it challenging to achieve fast inferences and are computationally heavy for real-time signal control implementation. In this paper, a simple yet accurate hybrid modeling method is proposed to predict traffic delay one-step ahead with the model made suitable for real-time implementation to control traffic flow. Since real-time road-side measurements are recorded in unstructured form, the paper also discusses other issues related to data extraction and the pre-processing process. Finally, a simple signal control loop is developed to demonstrate the proposed modeling approach, which has shown advantages in model accuracy and computation efficiency compared against several existing modeling methods.