Junior: The Stanford entry in the Urban Challenge

Montemerlo, Michael; Becker, Jan; Bhat, Suhrid; Dahlkamp, Hendrik; Dolgov, Dmitri; Ettinger, Scott; Haehnel, Dirk; Hilden, Tim; Hoffmann, Gabe; Huhnke, Burkhard; Johnston, Doug; Klumpp, Stefan; Langer, Dirk; Levandowski, Anthony; Levinson, Jesse; Marcil, Julien; Orenstein, David; Paefgen, Johannes; Penny, Isaac; Petrovskaya, Anna; Pflueger, Mike; Stanek, Ganymed; Stavens, David; Vogt, Antone; Thrun, Sebastian

doi:10.1002/rob.20258

Cited by 967 publications

(442 citation statements)

References 4 publications

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“…UVs are a typical complex system and have been involved in many technical fields in different disciplines such as cognitive science, AI, robotics, and vehicle engineering. They have been widely considered a universal experimental platform for verifying visual, auditory, cognitive, and AI technologies (Montemerlo et al, 2008). The development of UVs can not only improve the safety of driving and the efficiency of current transportation systems, but also play a significant role in other applications such as unmanned military combat platforms, polar and nuclear leak detection, and functions in other extreme environments.…”

Section: Trends In Unmanned Vehicle Developmentmentioning

confidence: 99%

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Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

125

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Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

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Section: Trends In Unmanned Vehicle Developmentmentioning

confidence: 99%

“…From 2004 to 2007, the American Defense Advanced Research Projects Agency (DARPA) organized three UV challenges, which promoted the rapid development of UV technologies (Bacha et al, 2008;Montemerlo et al, 2008;Urmson et al, 2008).…”

Section: Trends In Unmanned Vehicle Developmentmentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

125

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“…Historically, the mapping of features to costs has been performed by simple manual construction of a parameterized function, and then hand tuning various parameters to create a cost function that produces desired behavior. This manual approach has been frequently used whether the cost functions in question describe locations in the world [8,16,17] or actions to be performed [4,11,19]. Unfortunately, this tedious approach typically produces subpar results, potentially leading to subpar autonomous performance.…”

Section: Related Workmentioning

confidence: 99%

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

Silver

Bagnell

Stentz

2013

Springer Tracts in Advanced Robotics

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One of the many challenges in building robust and reliable autonomous systems is the large number of parameters and settings such systems often entail. The traditional approach to this task is simply to have system experts hand tune various parameter settings, and then validate them through simulation, offline playback, and field testing. However, this approach is tedious and time consuming for the expert, and typically produces subpar performance that does not generalize. Machine learning offers a solution to this problem in the form of learning from demonstration. Rather than ask an expert to explicitly encode his own preferences, he must simply demonstrate them, allowing the system to autonomously configure itself accordingly. This work extends this approach to the task of learning driving styles and maneuver preferences for an autonomous vehicle. Head to head experiments in simulation and with a live autonomous system demonstrate that this approach produces better autonomous performance, and with less expert interaction, than traditional hand tuning.

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“…Prototype cars utilizing machine vision can, under limited circumstances, drive fully autonomously on public highways (Dickmanns 2002), deserts (e.g. Thrun et al 2006) or urban environments (Montemerlo et al 2008;Wille et al 2010).…”

mentioning

confidence: 99%

Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations

et al. 2011

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Progress enables the creation of more automated and intelligent machines with increasing abilities that open up new roles between humans and machines. Only with a proper design for the resulting cooperative human-machine systems, these advances will make our lives easier, safer and enjoyable rather than harder and miserable. Starting from examples of natural cooperative systems, the paper investigates four cornerstone concepts for the design of such systems: ability, authority, control and responsibility, as well as their relationship to each other and to concepts like levels of automation and autonomy. Consistency in the relations between these concepts is identified as an important quality for the system design. A simple graphical tool is introduced that can help to visualize the cornerstone concepts and their relations in a single diagram. Examples from the automotive domain, where a cooperative guidance and control of highly automated vehicles is under investigation, demonstrate the application of the concepts and the tool. Transitions in authority and control, e.g. initiated by changes in the ability of human or machine, are identified as key challenges. A sufficient consistency of the mental models of human and machines, not only in the system use but also in the design and evaluation, can be a key enabler for a successful dynamic balance between humans and machines.

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Junior: The Stanford entry in the Urban Challenge

Cited by 967 publications

References 4 publications

Current trends in the development of intelligent unmanned autonomous systems

Current trends in the development of intelligent unmanned autonomous systems

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations

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