ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions

Mehra, Aryan; Mandal, Murari; Narang, Pratik; Chamola, Vinay

doi:10.1109/tits.2020.3013099

Cited by 88 publications

(32 citation statements)

References 47 publications

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“…For example, placing a yield sign in an intersection can change the behavior of the approaching vehicles. Hence, a comprehensive prediction module in AVs is critical to identify all position future motions to reduce collision hazards [ 12 , 13 ]. Although AD systems share many common challenges in real-world situations, they are differed noticeably in several aspects.…”

Section: Introductionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

435

133

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With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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Section: Introductionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

435

133

View full text Add to dashboard Cite

show abstract

“…Vehicles in a VANET can communicate with each other via Vehicle-to-Vehicle (V2V) communication and with RSUs via Vehicle-to-Infrastructure (V2I) communication. Researchers believe that with the help of VANET technology, we can overcome a lot of issues including, but not limited to, crash prevention and safety [4], driver assistance, and freeway management. For example, in case of an accident, the vehicles may broadcast the information to distant vehicles that may be planning to use the same route.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

Alladi¹,

Chamola²,

Sahu³

et al. 2022

Preprint

Self Cite

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Vehicular networks promise features such as traffic management, route scheduling, data exchange, entertainment, and much more. With any large-scale technological integration comes the challenge of providing security. Blockchain technology has been a popular choice of many studies for making the vehicular network more secure. Its characteristics meet some of the essential security requirements such as decentralization, transparency, tamper-proof nature, and public audit. This study catalogues some of the notable efforts in this direction over the last few years. We analyze around 75 blockchain-based security schemes for vehicular networks from an application, security, and blockchain perspective. The application perspective focuses on various applications which use secure blockchainbased vehicular networks such as transportation, parking, data sharing/ trading, and resource sharing. The security perspective focuses on security requirements and attacks. The blockchain perspective focuses on blockchain platforms, blockchain types, and consensus mechanisms used in blockchain implementation. We also compile the popular simulation tools used for simulating blockchain and for simulating vehicular networks. Additionally, to give the readers a broader perspective of the research area, we discuss the role of various state-of-the-art emerging technologies in blockchain-based vehicular networks. Lastly, we summarize the survey by listing out some common challenges and the future research directions in this field.

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“…For example, the presence of atmospheric haze can diminish the performance and accuracy of autonomous vehicles and surveillance systems. Mehra et al [20] showed that the presence of haze or any type of suspended particles in the atmosphere has an adverse snow noise effect on an image, degrading its brightness, contrast and texture features. Also, these suspended particles may sometimes alter the foreground and background of images and cause the failure of any type of computer vision task (e.g., VOT).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Robustness of Visual Object Tracking via Style Transfer

Amirkhani¹,

Barshooi²,

Ghahnavieh³

2022

Computers, Materials &Amp; Continua

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The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy of a system when it is exposed to an unseen noise-laden test dataset can be prevented by simply applying the style transfer technique on the train dataset and training the model with a combination of these and the original untrained data. To verify our proposed approach, it is applied on a generic object tracker by using regression networks. This method's validity is confirmed by testing it on an exclusive benchmark comprising 50 image sequences, with each sequence containing 15 types of noise at five different intensity levels. The OPE curves obtained show a 40% increase in the robustness of the proposed object tracker against noise, compared to the other trackers considered.

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ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions

Cited by 88 publications

References 47 publications

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

Enhancing the Robustness of Visual Object Tracking via Style Transfer

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