Applications of Machine Learning and Artificial Intelligence in Intelligent Transportation System: A Review

Gangwani, Divya; Gangwani, Pranav

doi:10.1007/978-981-16-3067-5_16

Cited by 37 publications

(18 citation statements)

References 46 publications

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“…At present, the discussions and concerns about smart cities are mainly focused on the technical level and the policy level: at the technical level, the concern is about the technology to be introduced and its feasibility; at the policy level, the discussion is about the government's attitude and guidance, and the presence of democratic forces is considered [ 12 ]. In addition, many scholars in China have been committed to constructing and filling the basic framework of smart city theory, trying to explore deeper connotations, combining local characteristics or even local features, and constructing a comprehensive and detailed theory from which all places can learn and form a system that truly fits our national conditions [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Recommendation and Application of Public Services in Smart Cities

Zhou

2022

Computational Intelligence and Neuroscience

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With the promotion and application of information technology, smart cities based on artificial intelligence have become the best choice for the government to solve urban problems, connect urban citizens, and provide quality public services. From the initial information city and digital city to the current smart city, the construction of smart cities has undergone profound changes with five major characteristics: big data, intelligence, innovation, interaction, and integration, and Internet giants have emerged in the field of public services in smart cities. Internet giants are emerging in the construction of public service platforms for smart cities, and traditional smart city construction enterprises are also expanding various forms of urban operation services through the form of “Internet+“. Nevertheless, there is still a gap between the quantity and quality of China’s smart cities compared with developed countries, and there is a need to build a number of pilot smart cities characterized by the linkage of artificial intelligence technology and public services, easy to promote, and sustainable development. The smart city construction model with public services as the core has research value and has the possibility of becoming the mainstream development in the future. Therefore, exploring the organic combination of AI technology and urban public services is the key to answer whether AI technology can promote the improvement of urban public services.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Recommendation and Application of Public Services in Smart Cities

Zhou

2022

Computational Intelligence and Neuroscience

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“…However, they as well assume a fully observable environment that can be modeled as a Markov Decision Process (MDP), and require a large number of interaction steps to converge. The partial-observability problem (POMDP) has been addressed in a number of works [25], [26], [27], [28], [29], [30], [31], [32], [33]. We adopt the common approach in these works, and use the observation history to aggregate belief of the partially observable state variables.…”

Section: Related Workmentioning

confidence: 99%

Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation

Yang¹,

Angleraud²,

Pieters³

et al. 2023

Preprint

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Robot control for tactile feedback based manipulation can be difficult due to modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq) Imitation Learning (IL) problem. The proposed Seq2Seq model first produces a robot-environment interaction sequence to estimate the partially observable environment state variables, and then, the observed interaction sequence is transformed to a control sequence for the task itself. The proposed Seq2Seq IL for tactile feedback based manipulation is experimentally validated on a door-open task in a simulated environment and a snap-on insertion task with a real robot. The model is able to learn both tasks from only 50 expert demonstrations while state-of-the-art reinforcement learning and imitation learning methods fail.

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“…Considering the insufficiency of prior knowledge from the environment, learning diverse policies can avoid converging to only one local solution and handle more irregular situations. A series of existing Quality-Diversity (QD) [18] diverse behaviors and diverse policies [19], [20], [21], [22], [23], [24], [25]. The MAP-Elites algorithms [26], [27] solve this problem by discretizing the behavior description space into a grid of cells.…”

Section: Introductionmentioning

confidence: 99%

Policy Dispersion in Non-Markovian Environment

Qu¹,

Cao²,

Yang³

et al. 2023

Preprint

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Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption that a reward only depends on the immediate state and action. However, a reward sometimes depends on the history of states and actions, which may result in the decision process in a non-Markovian environment. In such environments, agents receive rewards via temporally-extended behaviors sparsely, and the learned policies may be similar. This leads the agents acquired with similar policies generally overfit to the given task and can not quickly adapt to perturbations of environments. To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation. Specifically, we first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies. Finally, we prove that if the dispersion matrix is positive definite, the dispersed embeddings can effectively enlarge the disagreements across policies, yielding a diverse expression for the original policy embedding distribution. Experimental results show that this dispersion scheme can obtain more expressive diverse policies, which then derive more robust performance than recent learning baselines under various learning environments.

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Applications of Machine Learning and Artificial Intelligence in Intelligent Transportation System: A Review

Cited by 37 publications

References 46 publications

Artificial Intelligence-Based Recommendation and Application of Public Services in Smart Cities

Artificial Intelligence-Based Recommendation and Application of Public Services in Smart Cities

Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation

Policy Dispersion in Non-Markovian Environment

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