Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Turay, Tolga; Vladimirova, Tanya

doi:10.1109/access.2022.3147495

Cited by 30 publications

(14 citation statements)

References 265 publications

(442 reference statements)

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“…Data production without supervision would impact a model trained with redundant training sets. In order to recognise the environment in which predictable and unpredictable things are regarded, we must limit the data required to train the reinforcement learning model [30][31][32][33][34][35][36][37][38][39] to a set of data points.…”

Section: Related Workmentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN.

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Section: Related Workmentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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“…The Faster R-CNN [2] is selected for the implementation of the object detector due to being frequently used [1], [25], [46]. We choose two CNN models as the backbone network [17],…”

Section: Generalization Abilitymentioning

confidence: 99%

“…It has been shown that FPGAs outperform the other counterparts in terms of processing latency and power consumption [16], which is crucial in many autonomous applications, and in particular in Autonomous Driving Systems (ADSs) [17], [18]. At the same time, on-chip memory of automotive-grade FPGAs is limited, often amounting to less than 10MB [19]- [22] , which shows the pressing need for high-performance reduced size CNN models.…”

Section: Introductionmentioning

confidence: 99%

SSP Framework: A New Approach to Designing Lightweight Convolutional Neural Networks

Turay

Vladimirova

2022

IEEE Access

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“…Depending on how the radar was set up and the Doppler-FFT implementation, the Doppler processing time can range from milliseconds to dozens of milliseconds. On the other hand, neural network inference typically takes much longer and can be measured in tens or even hundreds of milliseconds [6][7][8]. Given the variety of sensors in modern automobiles, it is obvious that the CPU cannot make all decisions while still operating in real time.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Processing Architecture for Early Detection of Automotive Obstacles

Petrović,

Milovanović

2023

Electronics

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With the mass adoption of automotive vehicles, road accidents have become a common occurrence. One solution to this problem is to employ safety systems that can provide early warning for potential accidents. These systems alert drivers to brake or take active control of a vehicle in order to make braking safer and smoother, thereby protecting drivers and all other road traffic participants. Most such safety systems utilize millimeter-wave radar as primary sensors, and one of the main challenges is real-time data processing from multiple sensors integrated into a single passenger car. When an obstacle is too close to a vehicle, often there is insufficient time to run higher-order digital signal processing algorithms; hence, the decision to brake must be made based on low-level hardware processing only. For that purpose, a hardware generator for the early detection of automotive obstacles that does not impede the operation of higher-order signal processing algorithms is described. The proposed generator is captured in the Chisel hardware design language and a method for reducing the overall ranging latency is presented. The system constraints are calculated using an exemplary radar front-end and the proposed generator parameters. The obtained analytical results are experimentally confirmed with a prototype composed of a typical industrial radar front-end while the signal processing back-end instance of the described generator was implemented on an FPGA board. The measurements demonstrate that with the fast proximity alert, objects can be detected in less than a hundred microseconds, thus considerably reducing the system reaction delay and braking distance.

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Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 30 publications

References 265 publications

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

SSP Framework: A New Approach to Designing Lightweight Convolutional Neural Networks

Radar Signal Processing Architecture for Early Detection of Automotive Obstacles

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