Odin: Team VictorTango's entry in the DARPA Urban Challenge

Reinholtz, Charles F.; Hong, Dennis; Wicks, A. L.; Bacha, Andrew; Bauman, Cheryl; Faruque, Ruel; Fleming, Michael; Terwelp, Chris; Alberi, Thomas; Anderson, David E.; Cacciola, Stephen; Currier, Patrick; Dalton, Aaron; Farmer, Jesse; Hurdus, Jesse W.; Kimmel, Shawn; King, Peter; Taylor, Ashby B.; Covern, David Van; Webster, Mike

doi:10.1002/rob.20248

Cited by 250 publications

(118 citation statements)

References 14 publications

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“…The authors of [2] see testing as a "cornerstone of the development process" and use a simulation to test capabilities required for the Urban Challenge. Team VictorTango [3] agrees on the importance of simulation and testing. Their simulation can be configured by static scene files, too.…”

Section: Introductionmentioning

confidence: 84%

Testing autonomous driving systems against sensor and actuator error combinations

Minnerup

Knoll

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-Simulation as a tool for validating the behavior of an autonomous driving system can include sensor and actuator errors. A combination of such errors might lead to undesired behavior not occurring with a single error. In this paper we present a new concept for efficiently covering these error combinations. We propose to divide the simulation sequence into small separately simulated pieces that can be assembled to cover more patterns of error combinations. This way, parts of a simulation sequence that are similar for multiple error patterns are run only once. Our experimental results show that the concept can find weaknesses of planning and control systems and is faster than running a separate simulation sequence for each error pattern.

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Section: Introductionmentioning

confidence: 84%

Testing autonomous driving systems against sensor and actuator error combinations

Minnerup

Knoll

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…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

134

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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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“…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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Odin: Team VictorTango's entry in the DARPA Urban Challenge

Cited by 250 publications

References 14 publications

Testing autonomous driving systems against sensor and actuator error combinations

Testing autonomous driving systems against sensor and actuator error combinations

Current trends in the development of intelligent unmanned autonomous systems

Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration

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