A CNN-based Path Trajectory Prediction Approach with Safety Constraints

Zaman, Mostafa; Zohrabi, Nasibeh; Abdelwahed, Sherif

doi:10.1109/itec48692.2020.9161731

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Neural networks can automatically learn and capture complex features and patterns during vehicle driving by training on a large number of data samples. Trajectory prediction methods based on neural networks have been used in multi-vehicle collaborative planning [59][60][61][62][63]. As a variant of recurrent neural network (RNN), long-short term memory (LSTM) has a longer memory period and is suitable for long-term training of other vehicles.…”

Section: Multi-vehicle Trajectory Prediction Methodsmentioning

confidence: 99%

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Rong,

Meng,

Guo

et al. 2024

Sustainability

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Autonomous vehicles hold the potential to significantly improve traffic efficiency and advance the development of intelligent transportation systems. With the progression of autonomous driving technology, collaborative planning among multiple vehicles in autonomous driving scenarios has emerged as a pivotal challenge in realizing intelligent transportation systems. Serving as the cornerstone of unmanned mission decision-making, collaborative motion planning algorithms have garnered increasing attention in both theoretical exploration and practical application. These methods often follow a similar paradigm: the system initially discerns the driving intentions of each vehicle, subsequently assesses the surrounding environment, engages in path-planning, and formulates specific behavioral decisions. The paper discusses trajectory prediction, game theory, following behavior, and lane merging issues within the paradigm mentioned above. After briefly introducing the background of multi-vehicle autonomous driving, it provides a detailed description of the technological prerequisites for implementing these techniques. It reviews the main algorithms in motion planning, their functionalities, and applications in road environments, as well as current and future challenges and unresolved issues.

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Section: Multi-vehicle Trajectory Prediction Methodsmentioning

confidence: 99%

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Rong,

Meng,

Guo

et al. 2024

Sustainability

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show abstract

“…There are testbeds for the traffic management system that combine data analysis and model-based control to enhance performance, consisting of a network of connected vehicles, intersection controllers, data analysis services, and a variety of control services [75]. Other research focuses on vehicle routing, as travel time ambiguity affects identifying optimal routes and schedules on very congested urban roads and safe propagation using convolutional neural network [76].…”

Section: Intelligent Transportation Systemsmentioning

confidence: 99%

A Review of IoT-Based Smart City Development and Management

Zaman,

Puryear,

Abdelwahed

et al. 2024

Smart Cities

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Smart city initiatives aim to enhance urban domains such as healthcare, transportation, energy, education, environment, and logistics by leveraging advanced information and communication technologies, particularly the Internet of Things (IoT). While IoT integration offers significant benefits, it also introduces unique challenges. This paper provides a comprehensive review of IoT-based management in smart cities. It includes a discussion of a generalized architecture for IoT in smart cities, evaluates various metrics to assess the success of smart city projects, explores standards pertinent to these initiatives, and delves into the challenges encountered in implementing smart cities. Furthermore, the paper examines real-world applications of IoT in urban management, highlighting their advantages, practical impacts, and associated challenges. The research methodology involves addressing six key questions to explore IoT architecture, impacts on efficiency and sustainability, insights from global examples, critical standards, success metrics, and major deployment challenges. These findings offer valuable guidance for practitioners and policymakers in developing effective and sustainable smart city initiatives. The study significantly contributes to academia by enhancing knowledge, offering practical insights, and highlighting the importance of interdisciplinary research for urban innovation and sustainability, guiding future initiatives towards more effective smart city solutions.

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“…where Y t is the value at time t; β and θ are the AR and MA coefficients, respectively; and is the error term. Prophet [28,29], developed by Facebook, is a forecasting tool designed for daily datasets that exhibit strong seasonal patterns. It accounts for holidays and allows the addition of custom seasonalities, making it robust for various applications.…”

mentioning

confidence: 99%

LLM Multimodal Traffic Accident Forecasting

de Zarzà,

de Curtò,

Roig

et al. 2023

Sensors

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With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)—a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)—in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

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A CNN-based Path Trajectory Prediction Approach with Safety Constraints

Cited by 7 publications

References 27 publications

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

A Review of IoT-Based Smart City Development and Management

LLM Multimodal Traffic Accident Forecasting

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