Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the-art, delivering the best testing accuracy (98.38%), precision (0.937), and F1-measure (0.924) on the normal scene testing set, together with the best overall accuracy (98.36%) and precision (0.844) in the challenging scene test set, while the training time can be substantially shortened.Comment: 12 pages, 8 figures, under review by journal of IEEE Transactions on Intelligent Transportation System

Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture, Classification & Monitoring Scheme, and Site Selection Algorithm

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the first two modules, especially the module on site selection. Specifically, regarding the medical waste classification & monitoring module, three main entities, i.e., relevant government departments, hospitals, and logistics companies, are identified, which are involved in the five management functions of this module. Detailed data flow diagrams are provided to illustrate the information flow and the responsibilities of each entity. Regarding the site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system and the remaining route optimization module can be found at https://shorturl.at/cdY59.Comment: 8 pages, 6 figures, submitted to and under review by the IEEE Intelligent Vehicles Symposium (IV 2023

Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.Comment: 6 pages, 4 figures, under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

The seasonal foot printing mechanism of spring Arctic sea ice in the Bergen climate models

The influence of spring Arctic sea ice variability on the Pacific Decadal Oscillation (PDO) like sea surface temperature (SST) variability is established and investigated using an Atmosphere Ocean General Circulation Model (AOGCM) of the Bergen Climate Model version 2 (BCM2). The spring Arctic sea ice variability affects the mid-latitudes and tropics through the propagation of the anomalous Eliassen-Palm (E-P) flux from the polar region to mid- and low-latitudes during boreal spring. The pathway includes anomalous upward wave activity, which propagates to the high troposphere from near the surface of the polar region, turns southward between 500 hPa and 200 hPa and extends downward between 50°N and 70°N, influencing the near surface atmospheric circulation. The alteration of the near surface atmospheric circulation then causes anomalous surface ocean circulation. These circulation changes consequently leads to the SST anomalies in the North Pacific which may persist until the following summer, named seasonal “foot printing” mechanism (SFPM)

Isolation and characterization of an Aux/IAA gene (LaIAA2) from Larix

The phytohormone auxin controls many aspects of plant development. Auxin/indole-3-acetic acid (Aux/IAA)  transcriptional factors are key regulators of auxin responses in plants. To investigate the effects of auxin on  gene expression during the rooting process of Larix cuttings, a subtractive cDNA library was constructed and  272 UniEST were obtained by using suppression subtractive hybridization (SSH). Based on a fragment of 272  UniEST, the full-length cDNA of LaIAA2, an Aux /IAA gene from Larix was isolated. Then, the response  expression of LaIAA2 to auxin was determined by treating with different sources and concentration of auxin and cycloheximide and the expression patterns of LaIAA2 were examined in different tissues. The results show  that LaIAA2 appears to be the first response gene of auxin and LaIAA2 gene was involved in the root  development and auxin signaling. The express pattern of LaIAA2 gene indicated that it might play a central role in root development, specially regulated lateral and adventitious root production.Key words: Aux/IAA gene family, auxin, LaIAA2, Lari

Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers

Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging maneuvers. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving maneuvers besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving maneuvers and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving maneuvers with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.Comment: 6 pages, 3 figures, accepted by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Projected near-term changes of temperature extremes in Europe and China under different aerosol emissions

This study assesses near-term future changes in temperature extremes over China and Europe in scenarios with two very different anthropogenic aerosol (AA) pathways from 2016 to 2049: a maximum technically feasible aerosol reduction (MTFR), and a current legislation aerosol scenario (CLE), both with greenhouses gas forcing following RCP 4.5. Simulations with a fully coupled atmosphere-ocean model HadGEM3-GC2 show that there is an increase in hot extremes and a decrease in cold extremes relative to the present day (1995-2014) over China and Europe in both scenarios. However, the magnitude of the changes in both hot and cold extremes depends strongly on the AA pathway. The AA reduction in MTFR amplifies the changes in temperature extremes relative to CLE, and accounts for 40% and 30% of the projected changes in temperature extremes relative to present day over China and Europe respectively. Thus, this study suggests that future and current policy decisions about AA emissions have the potential for a large near-term impact on temperature extremes