Stadtpilot: First fully autonomous test drives in urban traffic

Nothdurft, Tobias; Hecker, P.; Ohl, Sebastian; Saust, Falko; Maurer, Markus; Reschka, Andreas; Böhmer, Jürgen Rüdiger

doi:10.1109/itsc.2011.6082883

Cited by 51 publications

(27 citation statements)

References 15 publications

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“…While most research and industry development is targeting urban related autonomous driving (e.g., [6], [7]), we research control methods for high-precision off-road path following. The idea is that the development of autonomous vehicles with specialized applications (e.g., mining sites) is facilitated by the lower requirements in terms of investment, infrastructure expansion and law adaptation, especially when compared to urban driving.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of economic model predictive control for an autonomous truck

Lima

Trincavelli

Nilsson

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a purepursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

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Section: Introductionmentioning

confidence: 99%

Experimental evaluation of economic model predictive control for an autonomous truck

Lima

Trincavelli

Nilsson

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…(Pomerleau and Jochem, 1996) detect lane markers in camera images to determine the position of autonomous driving cars on highways and the road curvature. Nowadays, also LiDAR systems are used to detect lane markers by their reflectivity (Nothdurft et al, 2011) (Yoneda et al, 2015). The detected markers are then matched to a digital map to improve the GNSS/INS position.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Localization by Lidar Point Correlation Improved by Change Detection

Schlichting

Brenner

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover,

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“…So z. B. bei der Fahrt auf der Bertha-Benz-Route des Karlsruher Instituts für Technologie und der Daimler AG [69], im Projekt Stadtpilot der Technischen Universität Braunschweig [41], [60] den Aktivitäten der Google Inc. [13], [59], dem Forschungsfahrzeug BRAiVE und dem VIAC Projekt des VisLab-Instituts der Università degli Studi di Parma [4], [8], den Forschungsaktivitäten des Sonderforschungsbereichs 28 der Deutschen Forschungsgemeinschaft [31], [55], [57] und den Resultaten bei der DARPA Urban Challenge [51], [52], [53]. Falls die Versuchsträger am öffentlichen Straßenverkehr teilnahmen, war immer ein Sicherheitsfahrer an Bord, der das technische System überwacht hat.…”

Section: Introductionunclassified

Sicherheitskonzept für autonome Fahrzeuge

Reschka

2015

Autonomes Fahren

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Stadtpilot: First fully autonomous test drives in urban traffic

Cited by 51 publications

References 15 publications

Experimental evaluation of economic model predictive control for an autonomous truck

Experimental evaluation of economic model predictive control for an autonomous truck

Vehicle Localization by Lidar Point Correlation Improved by Change Detection

Sicherheitskonzept für autonome Fahrzeuge

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