A Comprehensive Survey on Autonomous Driving Cars: A Perspective View

Devi, Satayu; Malarvezhi, P.; Dayana, R.; Vadivukkarasi, K.

doi:10.1007/s11277-020-07468-y

Cited by 30 publications

(11 citation statements)

References 43 publications

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“…For RODNet-CDC and RAMP-CNN model, the layers are all 3D; while for 4D-CDC model, all layers are 4D. 22 Based on the performance of camera object detection in [42], we infer that the acceptable/desired average precision and recall for radar would be 0.8 with different classes of objects. and robustness because the millimeter-wave is less attenuated by fog, rain, or snow.…”

Section: Discussionmentioning

confidence: 99%

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

et al. 2021

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Millimeter-wave (mmW) radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems (ADAS) by enabling robust and high-performance object detection, localization, as well as recognition-a key component of new environmental perception. In this paper, we propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts location and class of objects based on further processing of the rangevelocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upperbound with lower complexity. The extensive experiments show that the proposed RAMP-CNN model achieves better average recall (AR) and average precision (AP) than prior works in all testing scenarios (see Table.III). Besides, the RAMP-CNN model is validated to work robustly under the nighttime, which enables low-cost radars as a potential substitute for pure optical sensing under severe conditions.

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Section: Discussionmentioning

confidence: 99%

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

et al. 2021

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“…The European Defence Agency (EDA) have formally invited proposals for artificial intelligencebased network protection techniques in CAV [25]. For the past few years, Devi et al [26] reviewed machine learning approaches and methods used to develop autonomous driving systems. Every method's effectiveness is recorded and evaluated in terms of time taken for prediction with accuracy.…”

Section: Related Workmentioning

confidence: 99%

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Rajendar¹,

Kaliappan²

2022

Intelligent Automation &Amp; Soft Computing

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“…As modern data-driven and machine learning methods have become increasingly scalable and efficient, these methods have begun to be routinely used within autonomous applications. Particularly within perception tasks, machine learning models are frequently used to gain a semantic understanding of the objects within a vehicle's environment [38]. Unfortunately, these models are notoriously difficult to analyze [66,101].…”

Section: Vision Based Imitation Learningmentioning

confidence: 99%

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

Musau¹,

Hamilton²,

Lopez³

et al. 2022

Preprint

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Providing provable assertions of safety at runtime is crucial for autonomous systems.• Autonomous systems are tasked with operating in dynamic and uncertain environments.• Reachability analysis is adept in analyzing the correctness of uncertain systems.• The Simplex Architecture can be used to assure the safety of complex components.• Simplex approaches can be used to assure the safety of machine learning components.

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A Comprehensive Survey on Autonomous Driving Cars: A Perspective View

Cited by 30 publications

References 43 publications

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

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