Advisory warnings based on cooperative perception

Seeliger, Florian; Weidl, Galia; Petrich, Dominik; Naujoks, Frederik; Breuel, Gabi; Neukum, Alexandra; Dietmayer, Klaus

doi:10.1109/ivs.2014.6856479

Cited by 31 publications

(23 citation statements)

References 20 publications

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“…The challenge of understanding the situation and detecting the objects at intersections can potentially be solved using environment perception sensors with cooperative communication technology [5]. The remaining lack of information about the driver's future actions is still a crucial factor for early and reliable intersection assistance.…”

Section: B Need For Maneuver Informationmentioning

confidence: 99%

Predicting the driver's turn intentions at urban intersections using context-based indicators

Rodemerk

Winner

Kastner

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Many improvements have been made in the area of vehicle safety and pedestrian protection; however, urban intersections are still black spots for Advanced Driver Assistance Systems (ADAS). One of the main reasons is the uncertainty of the future driving direction at intersections. Due to that uncertainty, the early activation of an intersection ADAS will lead to high false positive rates, while, in contrast, a late activation of an intersection ADAS will lead to a low accident-reduction potential. This tradeoff is described as warning dilemma. In order to solve the warning dilemma, an approach to predicting the driver's turn intention at urban intersections is introduced. The novelty of the approach is its context-based prediction of the future driving maneuver several seconds before the driving trajectory changes. To predict maneuvers, indicators are used to encode the context information together with vehicle data. A system setup including prediction results of the system is described.

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Section: B Need For Maneuver Informationmentioning

confidence: 99%

Predicting the driver's turn intentions at urban intersections using context-based indicators

Rodemerk

Winner

Kastner

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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“…The relative pose is needed to align the received perception information to the global environment model in the ego-vehicle. The accuracy of the relative pose, especially the global orientation, has strong impact on the associability of the received perception information, see [1]. The global orientation error should not exceed a range of one degree for midranges.…”

Section: A Requirements On Localization Systemsmentioning

confidence: 99%

“…Within the evaluation "front center" is used as common RP. The lever arm is constructed by adopting the estimated object size, dependent on the RP combination, see [1]. The skew-symmetric matrix Ω bRA ebRA is constructed from the corresponding angular rates, see [20].…”

Section: B Processing Structurementioning

confidence: 99%

“…The prototype system described in [1] is based on the approximate CI variant Improved Fast Covariance Intersection (I-FCI) introduced in [26]. The method has low computational cost as it is based on a closed-form solution to determine ω.…”

Section: D) Fusionmentioning

confidence: 99%

“…III-C and IV-B. To find the best choice among the applicable fusion methods for an active road safety application described in [1], three variants of the Covariance Intersection (CI) method are compared.…”

Section: Introductionmentioning

confidence: 99%

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Inter-vehicle information-fusion with shared perception information

Seeliger

Dietmayer

2014

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

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Developing concepts for cooperative awareness was subject of several international research programs in the past years 2 . The main concept for cooperative awareness is the communication of localization information enriched by situation specific content by meanwhile standardized messages like the Cooperative Awareness Message (CAM) in Europe or the Basic Safety Message (BSM) in the US. The concept of cooperative perception extends the state of the art concept of cooperative awareness by integrating perception information from infrastructure perception systems (IPS) 3 and vehicle perception systems (VPS) in the ego-vehicles environment model. In this paper three variants of the Covariance Intersection (CI) method used for state fusion in an Inter-Vehicle Information-Fusion (IV-IF) System are compared. The goal is to figure out which CI method out of the selection is the best choice for an active road safety application described in a recent publication, which was developed within the research project Ko-PER (cooperative perception).

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From AI to CI: A Definition of Cooperative Intelligence in Autonomous Driving

Xiao

2020

Internet of Vehicles. Technologies and Services Toward Smart Cities

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Advisory warnings based on cooperative perception

Cited by 31 publications

References 20 publications

Predicting the driver's turn intentions at urban intersections using context-based indicators

Predicting the driver's turn intentions at urban intersections using context-based indicators

Inter-vehicle information-fusion with shared perception information

From AI to CI: A Definition of Cooperative Intelligence in Autonomous Driving

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