AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research &amp; Education

Samak, Tanmay Vilas; Samak, Chinmay Vilas; Kandhasamy, Sivanathan; Krovi, Venkat; Xie, Mingxing

doi:10.3390/robotics12030077

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Concerning resource management, simulators using Unity ML Agents demonstrate superior resource efficiency in comparison to others that have used ML packages such as Unreal Engine, TORCS, and CARLA. Unreal Engine is renowned for its robust rendering capabilities but often demands considerable resources due to its sophisticated physics systems [52,53]. On the contrary, Unity and its ML Agents optimise resource usage through techniques like batch processing, parallelization, and memory management, prioritizing efficiency even on modest hardware [54].…”

Section: Scenariomentioning

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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Section: Scenariomentioning

confidence: 99%

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

Crewe,

Humnabadkar,

Liu

et al. 2023

Sensors

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“…As the field of MARL gains momentum within the realm of autonomous vehicles, it is crucial to comprehensively examine the implications of both cooperative and competitive approaches. In this paper, we present AutoDRIVE Ecosystem [11], [12] as an enabler to develop physically accurate and graphically realistic digital twins of scaled autonomous vehicles viz. Nigel [13] and F1TENTH [14] in Section II.…”

Section: Introductionmentioning

confidence: 99%

“…We discussed representative case-studies for each behavior type and analyzed the training and deployment results. A natural extension of this work would be to analyze the sim2real [27] transfer of these trained policies.…”

mentioning

confidence: 99%

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

Samak

Xie

2021

2021 2nd International Conference on Control, Robotics and Intelligent System

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This work presents a modular and parallelizable multi-agent deep reinforcement learning framework for imbibing cooperative as well as competitive behaviors within autonomous vehicles. We introduce AutoDRIVE Ecosystem as an enabler to develop physically accurate and graphically realistic digital twins of Nigel and F1TENTH, two scaled autonomous vehicle platforms with unique qualities and capabilities, and leverage this ecosystem to train and deploy multi-agent reinforcement learning policies. We first investigate an intersection traversal problem using a set of cooperative vehicles (Nigel) that share limited state information with each other in single as well as multi-agent learning settings using a common policy approach. We then investigate an adversarial head-to-head autonomous racing problem using a different set of vehicles (F1TENTH) in a multi-agent learning setting using an individual policy approach. In either set of experiments, a decentralized learning architecture was adopted, which allowed robust training and testing of the approaches in stochastic environments, since the agents were mutually independent and exhibited asynchronous motion behavior. The problems were further aggravated by providing the agents with sparse observation spaces and requiring them to sample control commands that implicitly satisfied the imposed kinodynamic as well as safety constraints. The experimental results for both problem statements are reported in terms of quantitative metrics and qualitative remarks for training as well as deployment phases.

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“…It underscores global governmental actions and their significance in ensuring safe AV deployment, providing insights crucial for future transport systems. The[210] paper introduces AutoDRIVE, a comprehensive research and education ecosystem for autonomous driving and smart city solutions. AutoDRIVE prototypes, simulates, and deploys cyber-physical solutions, offering both software and hardware-in-the-loop interfaces.…”

mentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

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AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research & Education

Cited by 11 publications

References 26 publications

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

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

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

Localization and Mapping for Self-Driving Vehicles: A Survey

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