An intelligent energy management and traffic predictive model for autonomous vehicle systems

Manne, Suneetha; Lydia, E. Laxmi; Pustokhina, Irina V.; Pustokhin, Denis A.; Velmurugan, S.; Shankar, K.

doi:10.1007/s00500-021-05614-7

Cited by 21 publications

(8 citation statements)

References 20 publications

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“…Further, its network topology also gets altered regularly, since the node moves with changeable speed. VANET is incorporated in smart transportation systems [8] to decrease the time consumed on road which includes both waiting time and travel time at the intersection, for a commuter. Travel time could be decreased with the help of shortest paths between two nodes.…”

Section: Figure 1: Overview Of Vanetmentioning

confidence: 99%

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Gopi¹,

Mathapati²,

Prasad³

et al. 2022

Computers, Materials &Amp; Continua

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Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of 142 CMC, 2022, vol.72, no.1 the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

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Section: Figure 1: Overview Of Vanetmentioning

confidence: 99%

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Gopi¹,

Mathapati²,

Prasad³

et al. 2022

Computers, Materials &Amp; Continua

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“…Initialize the population of SSDA with random values for the MPC controller parameters "[T s , a, N, N P , r, q]" Evaluate the performance index and determine the global solution using (27) Update the positions using ( 28) that represent the MPC controller parameters "[T s , a, N, N P , r, q]" Determine the MPC controller gain vector "K mpc ") using…”

Section: Startmentioning

confidence: 99%

“…An improved particle swarm optimization (PSO) is applied instead of the conventional algorithms for path planning of mobile robot. 25,26 In Reference [27], the whale optimization algorithm (WOA) is utilized for the tuning of FL parameters for the energy management of AVs. In Reference [28], the GA, the memetics algorithms (MA), and mesh adaptive direct search (MADS) are applied to tune the gains of PID controller for AVs.…”

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confidence: 99%

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Elsisi

Ebrahim

2021

Int J of Intelligent Sys

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The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. Among different control strategies, the model predictive control (MPC) is devoted to providing efficacious steering control for the AVs. However, the formulation of MPC with a few parameters is the main challenge to overcome the complicated computation that happens due to the long prediction and control horizons. Hybridization between discrete‐time Laguerre function (DTLF) and MPC is suggested to formulate the MPC with few parameters. While the hybrid DTLF–MPC requires an adequate tuning technique for their gains to improve the system performance. A new low computational burden design is proposed in this paper to adjust the gains of the hybrid DTLF–MPC based on the social ski driver algorithm (SSDA). This algorithm is developed as a recently artificial intelligence technique. The tuned hybrid DTLF–MPC by SSDA is devoted to control AV's steering angle. Furthermore, the proposed system takes into account the vision dynamics. The system performance based on the tuned DTLF–MPC by SSDA is evaluated with the MPC based on the neural network algorithm for comparison. Different scenarios are carried out to confirm the superiority of the suggested control mechanism to handle small as well as large‐scale variations of road curvatures.

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“…Likewise, traffic flow-related environmental factors were taken into consideration to improve the accuracy of traffic flow prediction using BiLSTM models [30]. Other research also demonstrated an improved traffic flow prediction accuracy when using this model under connected and automated vehicle environments [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

Abduljabbar

Dia

Tsai

2021

Preprint

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Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 minutes into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25-100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

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An intelligent energy management and traffic predictive model for autonomous vehicle systems

Cited by 21 publications

References 20 publications

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

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