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Many cities across the world are looking to use technology and innovation to improve the overall efficiency and safety for their residents. At the heart of these smart-city plans, a variety of intelligent transportation system technologies can be used to improve safety, enhance mobility measures (e.g., traffic flow), and minimize environmental impacts of a city’s mobility ecosystem. Early implementations of these ITS technologies often take place in affluent cities, where there are many funding opportunities and suitable areas for deployment. However, it is critical that we also develop smart city solutions that are focused on improving conditions of disadvantaged and environmental justice communities, whose residents have suffered the most from unmitigated urban sprawl and its environmental and health impacts. As a leading example, Inland Southern California has grown to be one of the largest hubs of goods movement in the world. Numerous logistics facilities such as warehouses, rail facilities, and truck depots have rapidly spread throughout these communities, with the local residents bearing a disproportionate burden of truck traffic, poor air quality, and adverse health effects. Further, the majority of residents have lower-wage jobs and very few mobility options, other than low-end personal car ownership. To improve this situation, UC Riverside researchers have focused their smart city research on these impacted communities, finding innovative solutions to eco-friendly traffic management, developing better-shared (electric) mobility solutions for the community, improving freight movements, and enhancing the transition to vehicle electrification. Numerous research and development projects are currently underway in Inland Southern California, spanning advanced smart city modeling and impact analysis, community outreach events, and real-world technology demonstrations. This chapter describes several of these ITS solutions and their potential for improving many cities around the world. 

Connected and automated trucks (CATs) have the potential to transform the transportation system and logistics industry. Their unique features, such as operational strategies and truck driving behaviors, can affect transportation system performance. For successful development, testing and deployment of CATs, analysis, modeling, and simulation (AMS) plays an important role, especially in evaluating the impacts of CAT technologies on existing transportation systems. This paper presents a comprehensive review and assessment of up-to-date studies related to CAT AMS, focusing on three correlated elements: CAT applications, data, and tools. The research delves into CAT applications from individual CAT and CAT fleet to CAT-involved traffic. It explores available data sources relevant to CAT system use cases, assessing their potential issues and opportunities. The study also reviews existing AMS tools used to analyze CAT applications at both operational performance and network integration levels, emphasizing research needs in CAT-specific tools development. The findings identify the data needs and point out that existing AMS tools may not capture the complexity of CAT operation, which involves driving behaviors, vehicle-to-everything communications, autonomous capabilities, and response to truck-specific scenarios. The study will lay a solid foundation for further development of the AMS framework for CATs and provide guidance to future research of CAT applications. 

The battery electric truck (BET) has emerged as a promising solution to reduce greenhouse gas emissions in urban logistics, given the current strict environmental regulations. This research explores the formulation and solution of the bi-objective BET dispatching problem with backhauls and time windows, aiming to simultaneously reduce environmental impacts and enhance the efficiency of urban logistics. From the sustainability perspective, one of the objectives is to minimize total energy costs, which include energy consumption and battery replacement expenses. On the other hand, from an economic perspective, the other objective is the minimization of labor costs. To solve this bi-objective BET dispatching problem, we propose an innovative approach, integrating an adaptive large neighborhood search-based metaheuristics algorithm with a multi-objective optimization strategy. This integration enables the exploration of the trade-off between fleet energy expenses and labor costs, optimizing the dispatching decisions for BETs. To validate the proposed dispatching strategy, extensive experiments were conducted using real-world fleet operations data from a logistics fleet in Southern California. The results demonstrated that the proposed approach yields a set of Pareto solutions, showcasing its effectiveness in finding a balance between energy efficiency and labor costs in urban logistics systems. The findings of this research contribute to advancing sustainable urban logistics practices and provide valuable insights for fleet operators in effectively managing BET fleets to reduce environmental impacts while maintaining economic efficiency. 

Driver State Monitoring (DSM) is paramount for improving driving safety for both drivers of ego-vehicles and their surrounding road users, increasing public trust, and supporting the transition to autonomous driving. This paper introduces a Transformer-based classifier for DSM using an in-vehicle camera capturing raw Bayer images. Compared to traditional RGB images, we opt for the original Bayer data, further employing a Transformer-based classification algorithm. Experimental results prove that the accuracy of the Bayer Color-filled type images is only 0.61% lower than that of RGB images. Additionally, the performance of Bayer data is closely comparable to RGB images for DSM purposes. However, utilizing Bayer data can offer potential advantages, including reduced camera costs, lower energy consumption, and shortened Image Signal Processing (ISP) time. These benefits will enhance the efficacy of DSM systems and promote their widespread adoption. 

: In the challenging realm of object detection under rainy conditions, visual distortions significantly hinder accuracy. This paper introduces Rain-Adapt Faster RCNN (RAF-RCNN), an innovative end-to-end approach that merges advanced deraining techniques with robust object detection. Our method integrates rain removal and object detection into a single process, using a novel feature transfer learning approach for enhanced robustness. By employing the Extended Area Structural Discrepancy Loss (EASDL), RAF-RCNN enhances feature map evaluation, leading to significant performance improvements. In quantitative testing of the Rainy KITTI dataset, RAF-RCNN achieves a mean Average Precision (mAP) of 51.4% at IOU [0.5, 0.95], exceeding previous methods by at least 5.5%. These results demonstrate RAF-RCNN's potential to significantly enhance perception systems in intelligent transportation, promising substantial improvements in reliability and safety for autonomous vehicles operating in varied weather conditions. 

Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation, which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. Although an unprecedented evolution is now happening in the area of computer vision for object perception, state-of-the-art perception methods are still struggling with sophisticated real-world traffic environments due to the inevitable physical occlusion and limited receptive field of single-vehicle systems. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) is born to unlock the bottleneck of perception for driving automation. In this paper, we comprehensively review and analyze the research progress on CP, and we propose a unified CP framework. The architectures and taxonomy of CP systems based on different types of sensors are reviewed to show a high-level description of the workflow and different structures for CP systems. The node structure, sensing modality, and fusion schemes are reviewed and analyzed with detailed explanations for CP. A Hierarchical Cooperative Perception (HCP) framework is proposed, followed by a review of existing open-source tools that support CP development. The discussion highlights the current opportunities, open challenges, and anticipated future trends. 

A significant challenge in the field of object detection lies in the system's performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces "Feature Corrective Transfer Learning", a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model's ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm. 

Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of traffic signal controls, and interactions among different types of road users. To address these issues, we present in this paper a novel model called Knowledge-Informed Generative Adversarial Network (KI-GAN), which integrates both traffic signal information and multi-vehicle interactions to predict vehicle trajectories accurately. Additionally, we propose a specialized attention pooling method that accounts for vehicle orientation and proximity at intersections. Based on the SinD dataset, our KI-GAN model is able to achieve an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error (FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When the prediction window is extended to 9 seconds, the ADE and FDE values are further reduced to 0.11 and 0.26, respectively. These results demonstrate the effectiveness of the proposed KI-GAN model in vehicle trajectory prediction under complex scenarios at signalized intersections, which represents a significant advancement in the target field. 

The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This article addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries, where the deliveries need to be made following the last-in-first-out principle, and backhaul customers requiring pickups. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers while considering factors such as limited driving range, payload capacity of BETs, and the possibility of en route recharging. We formulate the problem as a mixed-integer linear programming model and propose an algorithm that combines adaptive large neighborhood search and simulated annealing metaheuristics to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 11% to 40%, while maintaining reasonable computational time. In addition, the proposed strategy provides solutions that are better than or comparable with those obtained by other metaheuristics. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a novel approach for dispatching BET fleets. The findings emphasize the potential of deploying BETs to achieve energy savings and advance the goal of green logistics.