As the potential for autonomous vehicles to be integrated on a large scale into modern traffic systems continues to grow, ensuring safe navigation in dynamic environments is crucial for smooth integration. To guarantee safety and prevent collisions, autonomous vehicles must be capable of accurately predicting the trajectories of surrounding traffic agents. Over the past decade, significant efforts from both academia and industry have been dedicated to designing solutions for precise trajectory forecasting. These efforts have produced a diverse range of approaches, raising questions about the differences between these methods and whether trajectory prediction challenges have been fully addressed. This paper reviews a substantial portion of recent trajectory prediction methods and devises a taxonomy to classify existing solutions. A general overview of the prediction pipeline is also provided, covering input and output modalities, modeling features, and prediction paradigms discussed in the literature. In addition, the paper discusses active research areas within trajectory prediction, addresses the posed research questions, and highlights the remaining research gaps and challenges.

--This paper introduces the Emirates Multi-T ask (EMT) dataset - the first publicly available dataset for autonomous driving collected in the Arab Gulf region. It contains over 30,000 frames from a dash-camera perspective, along with 570,000 annotated bounding boxes, covering approximately 150 kilometers of driving routes. The EMT dataset supports three primary tasks: tracking, trajectory forecasting and intention prediction. Each benchmark dataset is complemented with corresponding evaluations: (1) multi-agent tracking experiments, focusing on multi-class scenarios and occlusion handling; (2) trajectory forecasting evaluation using deep sequential and interaction-aware models; and (3) intention benchmark experiments conducted for predicting agents' intentions from observed trajectories. The dataset is publicly available at avlab.io/emt-dataset, and pre-processing scripts along with evaluation models can be accessed at github.com/A S autonomous driving technology advances, the ability of data-driven models to generalize across diverse road environments and conditions is essential for safe operation, but remains a significant challenge. To achieve robust generalization, it is critical to train models on datasets that capture a wide range of traffic scenes and characteristics. Current autonomous driving datasets provide extensive coverage of regions like the USA [1-5], Europe [6, 7], and parts of Asia, including China and Singapore [1, 8]. However, the Arab Gulf region, with its unique driving conditions, remains underrepresented. To address this gap, we introduce the Emirates Multi-Task (EMT) dataset, collected in the United Arab Emirates (UAE) to capture the region's distinct traffic conditions. This region offers diverse driving challenges due to its range of road layouts, including expansive highways, urban areas, and complex city junctions. Additionally, driving behavior in the UAE reflects a blend of modern regulations and traditional practices. This work was supported by Khalifa University of Science and Technology under A ward No. RIG-2023-117. The annotated dataset supports multiple benchmarks, including tracking, trajectory prediction, and intention prediction, aimed at advancing models robustness in complex driving environments. The tracking benchmark dataset is designed to evaluate the ability of algorithms to accurately identify and maintain consistent object tracking over time in a complex driving environment. Similar to current state-of-the-art (SOT A) tracking benchmarks [1, 9, 10], it focuses on the motion of vehicles, pedestrians, cyclists, and motorbikes, captured from a frontal camera perspective. The benchmark is designed to test tracking models under varying levels of traffic congestion and frequent lane changes. The dataset contains 8,806 unique tracking IDs, including 8,076 vehicles, 568 pedestrians, 158 motorbikes and 14 cyclists, and with a mean tracking duration of 6.5 seconds.

Abstract--In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for realworld imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers.