Literature survey for autonomous vehicles: sensor fusion, computer vision, system identification and fault tolerance

Mohamed, Amr; Ren, Jing; Gindy, Moustafa El; Lang, Haoxiang; Ouda, Ahmed Nasr

doi:10.1504/ijaac.2018.095104

Cited by 19 publications

(8 citation statements)

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“…The modified Kalman filter is the most popular method used for data fusion in robotics. However, the major drawback of this approach is the computation of the Jacobians which is extremely expensive, but some research has been made on this area to reduce the computational costs such as applying linearization, but this approach may introduce new errors in the filter and thus makes it more unstable [109]. Over the past few years, an extensive number of studies employed the Kalman filter approach in order to fuse data from different sources such as:…”

Section: The Kalman Filter Methodsmentioning

confidence: 99%

“…AVs' navigation is a difficult task because of a variety of obstacles such as the weather conditions [108]. These obstacles create uncertainty when the vehicle is unable to gain sufficient information from the surrounding environment, which makes it difficult for the vehicle to precisely follow a pre-defined path [109]. This section focuses on The Guardian, ABC News, USA Today, Fortune, CNBC [82][83][84][85][86] Tesla Utah, US May, 2018 Minor injury The driver was looking at her phone when the accident occurred, and the autopilot mode was on when the vehicle speeded up and hit a fire truck.…”

Section: Synopsis On the Technological Realization Of Avs (Sensor Fusmentioning

confidence: 99%

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Public acceptance and perception of autonomous vehicles: a comprehensive review

Othman

2021

AI Ethics

143

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Autonomous vehicles (AVs) or self-driving cars have the potential to provide many benefits such as improving mobility and reducing the energy and emissions consumed, travel time, and vehicle ownership. Thus, in the last few years, both research and industry have put significant efforts to develop AVs. However, laws and regulations are not ready yet for this switch and the legal sector is unable to take the lead but follow the development of AVs. Besides, the social acceptance is considered as a main key factor for the success of any new technology. Despite the enthusiastic speculation of AVs, little is known about the public acceptance and perception of the AVs technology or the factors that influence the public acceptance. This paper reviews the previous studies that focuses on testing the public acceptance and perception of AVs and sketches out the main trends in this area to provide some directions and recommendations for the future. This paper focuses on the influence of safety, ethics, liability, regulations, and the recent pandemic on the public acceptance of AVs.

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Section: The Kalman Filter Methodsmentioning

confidence: 99%

Section: Synopsis On the Technological Realization Of Avs (Sensor Fusmentioning

confidence: 99%

Public acceptance and perception of autonomous vehicles: a comprehensive review

Othman

2021

AI Ethics

143

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“…For example, in high-density traffic conditions, radar systems may pick up other vehicles' radar signals, causing false detections, interference and additional uncertainty (Hischke 2002;Schipper et al 2015). Moreover, as more sensors are integrated into vehicles, e.g., for supporting data fusion algorithms, the risk of faults increases as well as the power consumption (Ren et al 2018;Gawron et al 2018). A popular approach to optimize AVs energy consumption is the adoption of V2V and V2I communications (Gawron et al 2018;Guanetti, Kim, and Borrelli 2018); however, correctly supporting these capabilities in AVs remains still a challenge (Gruyer et al 2017;Van Brummelen et al 2018).…”

Section: Autonomous Vehicles Perceptionmentioning

confidence: 99%

Adaptive monitoring for autonomous vehicles using the HAFLoop architecture

Zavala

Franch

Marco

et al. 2020

Enterprise Information Systems

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Current Self-Adaptive Systems (SASs) such as Autonomous Vehicles (AVs) are systems able to deal with highly complex contexts. However, due to the use of static feedback loops they are not able to respond to unanticipated situations such as sensor faults. Previously, we have proposed HAFLoop (Highly Adaptive Feedback control Loop), an architecture for adaptive loops in SASs. In this paper, we incorporate HAFLoop into an AV solution which leverages machine learning techniques to determine the best monitoring strategy at runtime. We have evaluated our solution using real vehicles; results are promising and demonstrate the great potential of our proposal.

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“…A combination of RADARs, LiDARs, and cameras is used to detect, classify, and track the surrounding obstacles (Zhu et al, 2017). Computer vision is a popular approach due to the low cost of cameras and the ability to classify the obstacles accurately (e.g., Mohamed et al, 2018;Janai et al, 2020). Machine learning approaches for environment abstraction are on the rise and appear promising (e.g., Yang et al, 2019;Fayyad et al, 2020;Sligar, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Sharath

Mehran

2021

Front. Future Transp.

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The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

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Literature survey for autonomous vehicles: sensor fusion, computer vision, system identification and fault tolerance

Cited by 19 publications

References 0 publications

Public acceptance and perception of autonomous vehicles: a comprehensive review

Public acceptance and perception of autonomous vehicles: a comprehensive review

Adaptive monitoring for autonomous vehicles using the HAFLoop architecture

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

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