Stanley: The robot that won the DARPA Grand Challenge

Thrun, Sebastian; Montemerlo, Michael; Dahlkamp, Hendrik; Stavens, David; Aron, Andrei; Diebel, James; Fong, Philip W. L.; Gale, John T.; Halpenny, Morgan; Hoffmann, Gabriel; Lau, Kenny; Oakley, Celia M.; Palatucci, Mark; Pratt, Vaughan R.; Stang, Pascal; Strohband, Sven; Dupont, Cedric; Jendrossek, Lars-Erik; Koelen, Christian; Markey, Charles; Rummel, Carlo; Niekerk, Joe van; Jensen, Eric L. N.; Alessandrini, Philippe; Bradski, Gary; Davies, Bob; Ettinger, Scott; Kaehler, Adrian; Nefian, Ara; Mahoney, Pamela

doi:10.1002/rob.20147

Cited by 1,768 publications

(748 citation statements)

References 12 publications

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“…the Stanley method is expressed as follows [17]: In order to rapidly converge to the defined path, the Stanley method not only considers the heading error, but also transfers the perpendicular error between the vehicle and the path into an angular error in order for the vehicle to quickly intersect with the path.…”

Section: Stanley Methodsmentioning

confidence: 99%

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Shan

Yang

Chen

et al. 2015

International Journal of Advanced Robotic Systems

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Simple and efficient geometric controllers, like PurePursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CFPursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C 1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6mcurvature, 9mcurvature and 12mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.

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Section: Stanley Methodsmentioning

confidence: 99%

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Shan

Yang

Chen

et al. 2015

International Journal of Advanced Robotic Systems

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“…Thrun et al [8] describes Stanley, the winner of the 2005 DARPA Grand Challenge. The robot was built by Stanford University researchers and uses laser range finders as well as RADAR sensors for sensing the environment.…”

Section: Related Workmentioning

confidence: 99%

Mapping the Surroundings as a Requirement for Autonomous Driving

Steininger

Stephan

Böhm

et al. 2016

APP

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Abstract. Motivated by the hype around driverless cars and the challenges of the sensor integration and data processing, this paper presents a model for using a XBox One Microsoft Kinect stereo camera as sensor for mapping the surroundings. Today, the recognition of the environment of the car is mostly done by a mix of sensors like LiDAR, RADAR and cameras. In the case of the outdoor delivery challenge Robotour 2016 with model cars in scale 1 : 5, it is our goal to solve the task with one camera only. To this end, a three-stage approach was developed. The test results show that our approach can detect and locate objects at a range of up to eight meters in order to incorporate them as barriers in the navigation process.

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“…Several teams successfully developed a vehicle that has the ability to drive itself and achieve the assigned task. As a result, the self-driving car or autonomous vehicle is now successfully developed by many research groups either in universities or more recently by private companies [1][2][3]. They proved the performance of driving in a realtraffic environment, autonomously with the capability of safety.…”

Section: Introductionmentioning

confidence: 99%

Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication

Wuthishuwong

Traechtler

Bruns

2015

J Wireless Com Network

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The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future. The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system (ITS). The fundamental objectives of this work are to manage autonomous vehicles crossing an intersection with no collisions, maintaining that a vehicle drives continuously, and to decrease the waiting time at an intersection. In this paper, a discrete model of the one-way single intersection is designed. The vehicle-to-infrastructure (V2I) communication is implemented to exchange information between a vehicle and an intersection manager which is the roadside infrastructure. The safe trajectory of autonomous vehicles for the autonomous intersection management is determined and presented by using discrete mathematics.

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Stanley: The robot that won the DARPA Grand Challenge

Cited by 1,768 publications

References 12 publications

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Mapping the Surroundings as a Requirement for Autonomous Driving

Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication

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