Real-time vehicle navigation using modified A* algorithm

Nayak, Snehal; Narvekar, Meera

doi:10.1109/etiict.2017.7977021

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…When the AV enters an intersection, the time-to-reach (TTR) variable is calculated for both ego and north, which depends on their location and speed they are traveling with. The TTR is widely used for maneuver safety assessment [39], in vehicle safety systems like adaptive cruise control and collision avoidance. This index allows for estimating time when two moving objects will collide given their routs intersect.…”

Section: Case Studymentioning

confidence: 99%

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Aksjonov¹,

Kyrki²

2022

Preprint

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While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a single system. This paper proposes a decision making and control framework, which profits from advantages of both the rule-and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. A rule-based switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized every time, when the Learned one does not meet the safety constraint, and also directly participates in the safe Learned controller training. Decision making and control in autonomous driving is chosen as the system case study, where an autonomous vehicle learns a multi-task policy to safely cross an unprotected intersection. Multiple requirements (i.e., safety, efficiency, and comfort) are set for vehicle operation. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environment is successfully demonstrated.

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Section: Case Studymentioning

confidence: 99%

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Aksjonov¹,

Kyrki²

2022

Preprint

View full text Add to dashboard Cite

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“…Enhancing the traffic management. In the context of reducing traffic congestion, we find (Horng, 2014) and (Karatzoglou et al, 2017), which recommend whether a given vehicle should change routes or not, aiming to minimize the possibility of congestion in an entire urban area, and (Kong et al, 2018) and (Nayak and Narvekar, 2017), whose recommender system analyzes traffic flow data guiding users to reach a location and helping resource management such as time or fuel. Addressing parking management problems, Horng (2015Horng ( , 2014; Yavari et al (2016); Hassani et al (2018); Gang (2018) provide parking spaces recommendations.…”

Section: Recommender Systems and Smart Mobilitymentioning

confidence: 99%

Recommender systems for smart cities

Quijano-Sánchez

Cantador

Cortés-Cediel

et al. 2020

Information Systems

101

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“…Municipal corporations often present irregularity in the disposal of waste present in the dustbins located in different areas, which utilizes more human effort, time, and cost. In existing research, the shortest path algorithm [11] and dynamic shorter path routing model [12] have been used for the collection of waste. Though these algorithms provide information about waste, they have some significant drawbacks; (i) They do not provide real-time information about waste; (ii) each bin has to be manually checked; (iii) they require more human effort, time, cost, and resources; and (iv) one must manually take off the lid to check the level of waste.…”

Section: Introductionmentioning

confidence: 99%

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

et al. 2020

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Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

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Real-time vehicle navigation using modified A* algorithm

Cited by 4 publications

References 15 publications

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Recommender systems for smart cities

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

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