Up to the limits: Autonomous Audi TTS

Funke, Joseph; Theodosis, Paul A.; Hindiyeh, Rami Y.; Stanek, Ganymed; Kritatakirana, Krisada; Gerdes, Chris; Langer, Dirk; Hernandez, Marcial; Müller-Beßler, Bernhard; Huhnke, Burkhard

doi:10.1109/ivs.2012.6232212

Cited by 106 publications

(50 citation statements)

References 3 publications

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“…Several approaches have been taken to the problem of aggressive autonomous driving. In [6], an analytic approach is explored. The performance limits of a vehicle are pushed using a simple model-based feedback controller and extensive pre-planning to follow a racing line around a track.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based High-Speed Driving With a Deep Dynamic Observer

Drews

Williams

Goldfain

et al. 2019

IEEE Robot. Autom. Lett.

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In this paper we present a framework for combining deep learning-based road detection, particle filters, and Model Predictive Control (MPC) to drive aggressively using only a monocular camera, IMU, and wheel speed sensors. This framework uses deep convolutional neural networks combined with LSTMs to learn a local cost map representation of the track in front of the vehicle. A particle filter uses this dynamic observation model to localize in a schematic map, and MPC is used to drive aggressively using this particle filter based state estimate. We show extensive real world testing results, and demonstrate reliable operation of the vehicle at the friction limits on a complex dirt track. We reach speeds above 27 mph (12 m/s) on a dirt track with a 105 foot (32m) long straight using our 1:5 scale test vehicle.

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Section: Related Workmentioning

confidence: 99%

Vision-Based High-Speed Driving With a Deep Dynamic Observer

Drews

Williams

Goldfain

et al. 2019

IEEE Robot. Autom. Lett.

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“…High-speed car control was tested by Stanford [5] in real life scenarios in Pikes Peak Rally drive. Autonomous operations in real scenarios were targeted by many others like Google [6], Braunschweig University [7], and Berlin University [8], which create new milestones in autonomous driving.…”

Section: Adas and Autonomous Drivingmentioning

confidence: 99%

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Dheekonda

Panda

Khan

et al. 2017

SAE Technical Paper Series

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Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object's description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time.

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“…Collisions should, in any event, be very rare events. Through careful control system design, automated cars could conceivably avoid any collisions that are avoidable within the constraints placed by the laws of physics [17,18]. In those rare cases where collisions are truly unavoidable, society might accept suboptimal outcomes in return for the clarity and comfort associated with automated vehicles that possess a clear respect for human life above other priorities.…”

Section: Constraints and Deontological Ethicsmentioning

confidence: 99%