Generation of high precision digital maps using circular arc splines

Schindler, Andreas; Maier, Georg; Janda, Florian

doi:10.1109/ivs.2012.6232124

Cited by 33 publications

(25 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A pure geometric representation for example as presented in [3] models road markings using arc splines. In RoadGraphs [4] streets are represented by a graph structure.…”

Section: Related Workmentioning

confidence: 99%

Semantic state space for high-level maneuver planning in structured traffic scenes

Kohlhaas

Bittner

Schamm

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Originating from simple cruise control systems that monitor and control the speed of the vehicle, driver assistance systems have evolved into intelligent systems. Future assistance systems will combine information from different sensors and data sources to build up a model of the current traffic scene. This way they will be able to assist in challenging tasks in complex situations. Towards this goal, we present a semantic scene representation for modeling traffic scenes. Based on a geometric representation a semantic representation is defined using an ontology to model relevant traffic elements and relations. Considering potential relations of the ego vehicle, a semantic state space of the ego vehicle is derived. Transitions are defined that model state changes (maneuvers). The model can be used for example for situation analysis and high level planning for driving hint generation or automated driving. The method is evaluated in different traffic situations and on real sensor data. It is going to be applied to (semi-)automated driving in a real test vehicle.

show abstract

“…A pure geometric representation for example as presented in [3] models road markings using arc splines. In RoadGraphs [4] streets are represented by a graph structure.…”

Section: Related Workmentioning

confidence: 99%

Semantic state space for high-level maneuver planning in structured traffic scenes

Kohlhaas

Bittner

Schamm

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…The digital map model used for the present selflocalization approach has been presented in [10]. Continuous road elements like individual lanes and road markings are represented by smooth arc splines, which are curves composed by smoothly joint circular arcs and line segments as depicted in Figure 2.…”

Section: Digital Mapmentioning

confidence: 99%

Vehicle self-localization with high-precision digital maps

Schindler

2013

2013 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)

Self Cite

View full text Add to dashboard Cite

Cooperative driver assistance functions benefit from sharing information on the local environments of individual road users by means of communication technology and advanced sensor data fusion methods. However, the consistent integration of environment models as well as the subsequent interpretation of traffic situations impose high requirements on the self-localization accuracy of vehicles. This paper presents methods and models for a map-based vehicle self-localization approach. Basically, information from the vehicular environment perception (using a monocular camera and laser scanner) is associated with data of a high-precision digital map in order to deduce the vehicle's position. Within the Monte-Carlo localization approach, the association of road markings is reduced to a prototype fitting problem which can be solved efficiently due to a map model based on smooth arc splines. Experiments on a rural road show that the localization approach reaches a global positioning accuracy in both lateral and longitudinal direction significantly below one meter and an orientation accuracy below one degree even at a speed up to 100 km/h in real-time.

show abstract

“…Für den Nachweis, dass γ ∈ S, bleibt dann lediglich zu prü-fen, dass γ keine Selbstüberschneidungen hat. Zu diesem Zweck betrachten wir für 1 ≤ i ≤ l die Strecke λ i mit Alternanten a (1) i < a (2) i . Aufgrund der Extremalität der λ i gilt: x i ≤ a (2) i ≤ y i bezüglich der Orientierung von λ i und somit ist γ eine einfache Kurve (vgl.…”

Section: Integration Von Streckenunclassified

“…Für die Wahl des Kurvenmodells sind aus Anwendungssicht sowohl die Genauigkeit als auch die effektive Beschreibbarkeit entscheidend. Als ein Beispiel für die Generierung von Kartenmaterial, dessen Informationsgehalt weit über den momentanen Standard hinausgeht, sei [1] genannt.…”

Section: Introductionunclassified

Integration von Strecken in die Kreisbogensplinepassung mit optimaler Segmentzahl / Detecting Straight Sections and Optimal Arc Spline Approximation

Maier

Schindler

Janda

et al. 2013

tm - Technisches Messen

Self Cite

View full text Add to dashboard Cite

Zusammenfassung Viele Anwendungen erfordern eine kompakte und effiziente Kurvenrepräsentation von Daten. Kreisbogensplines -Kurven, die stückweise aus glatt aneinander gesetzten Kreisbögen und Strecken bestehen -überzeugen aus Anwendungssicht durch ihre effektive Beschreibbarkeit. Das SMAP-Verfahren garantiert unter Einhaltung einer einstellbaren maximalen Abweichung einen segmentzahl-minimalen Kreisbogenspline. Um etwa den Anforderungen bei der Erzeugung digitaler Karten gerecht zu werden, wird eine Erweiterung des Verfahrens vorgestellt, die Liniensegmente an geeigneten Stellen in die Lösungskurve integriert.Summary Various applications require a compact and efficient data representation by curves. Arc splines are curves consisting of piecewise smoothly joined arcs and line segments. From a practical point of view, they stand out due to their efficient descriptiveness. The SMAP approach guarantees an arc spline with a minimal number of segments for any given user-specified tolerance. For instance, to satisfy the requirements for generating digital maps, we introduce an extension which integrates line segments into the solution curve at appropriate positions.

show abstract

Generation of high precision digital maps using circular arc splines

Cited by 33 publications

References 10 publications

Semantic state space for high-level maneuver planning in structured traffic scenes

Semantic state space for high-level maneuver planning in structured traffic scenes

Vehicle self-localization with high-precision digital maps

Integration von Strecken in die Kreisbogensplinepassung mit optimaler Segmentzahl / Detecting Straight Sections and Optimal Arc Spline Approximation

Contact Info

Product

Resources

About