HetroTraffSim: A Macroscopic Heterogeneous Traffic Flow Simulator for Road Bottlenecks

Zeb, Ali; Khattak, Khurram S.; Ullah, Muhammad Rehmat; Khan, Zahoor; Gulliver, Thomas Aaron

doi:10.3390/futuretransp3010022

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…In fact, as mentioned in [59][60][61], the concept of heterogenous traffic flow is explored. It concerns utilizing different car-truck, truck-truck, and car-car combination effects to develop pragmatic cross-class interaction rules.…”

Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

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Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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“…One of them is the deployment of acceleration structures like bounding volume hierarchy (BVH), allowing for the efficient organization of the scene's geometry and rapid culling of irrelevant portions for ray-object intersection tests. By spatially partitioning the scene into manageable chunks, Unity significantly reduces the number of checks required during raycasts [60]. Moreover, optimized algorithms, caching mechanisms, and SIMD (single instruction, multiple data) optimization ensure that ray-object intersections are calculated with minimal computational overhead.…”

Section: Perceptionmentioning

confidence: 99%

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation

Crewe,

Humnabadkar,

Liu

et al. 2023

Sensors

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With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.

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“…The examination of traffic flow is inherently complex [1], especially when speeds decrease in congestion scenarios [2]. To comprehend and measure traffic flow dynamics, three principal methods are utilized.…”

Section: Introductionmentioning

confidence: 99%

Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram

Raufi,

maniat

2024

Preprint

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The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

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HetroTraffSim: A Macroscopic Heterogeneous Traffic Flow Simulator for Road Bottlenecks

Cited by 5 publications

References 18 publications

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation

Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram

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