Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor

Li, Yanjun; Yin, Guodong; Zhuang, Weichao; Zhang, Ning; Wang, Jinxiang; Geng, Keke

doi:10.1109/tsmc.2018.2850367

Cited by 35 publications

(5 citation statements)

References 52 publications

(53 reference statements)

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“…This deep learning network can detect the hidden states, which are normally difficult to be measured by sensors. In [78], a CNN and LSTM-based observer is presented. First, LSTM processes videos and adds a temporal dimension to the cost map.…”

Section: Deep Learning For Control Algorithmsmentioning

confidence: 99%

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Huang¹,

Ren²

2022

J. Engg. Res. & Sci.

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Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking. The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research. To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.

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Section: Deep Learning For Control Algorithmsmentioning

confidence: 99%

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Huang¹,

Ren²

2022

J. Engg. Res. & Sci.

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“…T HE NUMBER of passenger cars and commercial vehicles on urban roads has increased rapidly in the past decade, which inevitably causes congestion and numerous accidents [1], [2], [3], [4], [5], [6]. Intersections are the primary source of congestion in an urban transportation system [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Cao

Tang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Replacing traffic signals with roadside vehicle-toinfrastructure systems in the era of connected and autonomous vehicles (CAVs) is promising. Managing CAVs in a signal-free intersection, known as autonomous intersection management (AIM), controls the driving behavior of each intersection-traverse CAV to maximize the throughput. Although AIM improves the gross throughput, the fairness of each individual vehicle in its right of way is not seriously considered. This study sets up an AIM system in the cyber-physical-social space to trade traverse priorities quantitatively and fairly. To that end, one needs an AIM method that is optimal and stable, otherwise no convincing trades of traverse priorities could be made. This study proposes a near-optimal lane-free AIM method based on numerical optimal control, wherein log-exp functions are deployed to convexify nondifferentiable collision-avoidance constraints. Besides that, a parameterized social force model (SFM) is proposed to provide a tunable initial guess for numerical optimal control. By tuning the urgency weights in SFM, one may get cooperative trajectories in different homotopy classes, which are further utilized to decide the amount of virtual currency to reward those CAVs who tend to share their traverse priorities. The overall method improves the traverse throughput with individual fairness Manuscript

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“…The excellent performance of CNNs comes from their wider and deeper models [4]; however, these models have also faced an increasing memory burden [21], which limits their application in resource-constrained and high real-time requirement scenarios, such as mobile terminals and embedded systems with low hardware resources [22,23].…”

Section: Introductionmentioning

confidence: 99%

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Zhou

Chang

et al. 2022

Applied Sciences

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Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge the receptive field, but this increases the parameters. Most CNN models use pooling layers to extract important features, but the pooling operations cause information loss. Transposed convolution can increase the spatial size of the feature maps to recover the lost low-resolution information. In this study, we used two branches with different dilated rates to obtain different size features. The dilated convolution can capture richer information, and the outputs from the two channels are concatenated together as input for the next block. The small size feature maps of the top blocks are transposed to increase the spatial size of the feature maps to recover low-resolution prediction maps. We evaluated the model on three image classification benchmark datasets (CIFAR-10, SVHN, and FMNIST) with four state-of-the-art models, namely, VGG16, VGG19, ResNeXt, and DenseNet. The experimental results show that CDTNet achieved lower loss, higher accuracy, and faster convergence speed in the training and test stages. The average test accuracy of CDTNet increased by 54.81% at most on SVHN with VGG19 and by 1.28% at least on FMNIST with VGG16, which proves that CDTNet has better performance and strong generalization abilities, as well as fewer parameters.

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Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor

Cited by 35 publications

References 52 publications

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

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