Sensor and Object Recognition Technologies for Self-driving Cars

Hirz, Mario; Walzel, Bernhard

doi:10.14733/cadconfp.2017.170-175

Cited by 9 publications

(7 citation statements)

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“…If the focus is on the question of 'transparency for whom' and the transparency measures to be taken by ed-tech companies when developing AI are treated separately from the information that they need to share with various stakeholders of education, then it can be noticed that the above critique is mostly directed at the information shared with end-users, not the measures to be taken by ed-tech companies. For example, the autopilots working in cars are powered by stateof-the-art image recognition algorithms trained on vast amounts of data (Hirz and Walzel;. When drivers are using the autopilots, they do not necessarily want to know the details of how AI is making every decision or identifying different road signals etc.…”

Section: Discussionmentioning

confidence: 99%

A Transparency Index Framework for AI in Education

Chaudhry¹,

Cukurova²,

Luckin³

2022

Preprint

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Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.

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Section: Discussionmentioning

confidence: 99%

A Transparency Index Framework for AI in Education

Chaudhry¹,

Cukurova²,

Luckin³

2022

Preprint

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“…This includes code for transmission control, cruise control, collision mitigation, connectivity, engine control and many other basic and advanced functionalities that come with the cars bought today. Similarly, the cars now have more advanced hardware, which includes plethora of sensors that ensure vehicles are able to perceive the world around them just like humans do [18]. Therefore, the complexity of the modern age vehicle is the result of both more advanced hardware and software needed to process the information retrieved from the environment and for decision making capability.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

A Survey on Simulators for Testing Self-Driving Cars

Kaur

Taghavi

Tian

et al. 2021

2021 Fourth International Conference on Connected and Autonomous Driving (MetroCAD)

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A rigorous and comprehensive testing plays a key role in training self-driving cars to handle variety of situations that they are expected to see on public roads.The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap, however, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-theart simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we also present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.

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“…The minimum equipment in this case is composed of long-and short-range radars, LIDAR, and cameras. Each of these components has strengths and weaknesses, mostly covered by the other onboard sensors as explained in [19]. The hypothesis in this case is that, by 2025, the sensor suite is going to correctly perceive the surroundings of the vehicle with no system failures, as long as the ODD conditions are met, reaching a full awareness.…”

Section: Equipment Overview and Effectiveness Of The Systemmentioning

confidence: 99%

Safety on the Italian Highways: Impacts of the Highway Chauffeur System

Agriesti

Studer

Gandini

et al. 2019

Smart Innovation, Systems and Technologies

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Road safety is certainly a main drive toward automated driving. Removing the human factor from driving maneuvers allows to prevent crashes caused or fostered by human flaws, such as distraction or tiredness. However, full automation on public roads is still a goal to be achieved, and different solutions are currently being developed. In the short term, one of the most promising examples is the Highway Chauffeur system, capable of performing fully automated driving in defined conditions and environments. The scope of this paper is to evaluate the number of crashes that could possibly be addressed through a defined market penetration of Highway Chauffeur vehicles (HC vehicles in the article). The evaluation is based on a bibliographical review and on an analysis of crash-related data recorded in Italy by the National Statistical Institute-ISTAT-on public roads. By using the information publicly available on ISTAT's website, this paper provides a first magnitude concerning the number of crashes addressable by the Highway Chauffeur system, and final considerations on the future research to be carried out.

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Sensor and Object Recognition Technologies for Self-driving Cars

Cited by 9 publications

References 0 publications

A Transparency Index Framework for AI in Education

A Transparency Index Framework for AI in Education

A Survey on Simulators for Testing Self-Driving Cars

Safety on the Italian Highways: Impacts of the Highway Chauffeur System

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