Advanced Intersection Safety - The EC project INTERSAFE

Fuerstenberg, Kay; Roessler, Bernd

doi:10.1109/ivs.2006.1689610

Cited by 9 publications

(7 citation statements)

References 2 publications

(2 reference statements)

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“…The Intersafe-2 project was a successor to Intersafe. The focus to develop Cooperative Intersection Safety Systems led to a series of demonstrated functions among which Fürstenberg and Rössler showed emergency braking in crossing traffic [5]. The system either increased a driver initiated braking in high risk situations or applied full force automatically when a collision became unavoidable.…”

Section: A Collision Mitigation At Intersectionsmentioning

confidence: 99%

Collision mitigation for crossing traffic in urban scenarios

Heck

Bellin

Matousek

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Current collision mitigation systems focus on rear end collisions. To address the full spectrum of real world accidents, these systems could be enhanced to cover more traffic situations. Vehicle to vehicle accidents in crossing traffic situations make up around 25% of accidents in Germany. This paper discusses the requirements and differences compared to rear-end collisions. Presented here is an action concept that takes into account how the impact configuration is changed by breaking the host (impacting) vehicle. Based on this concept the requirements for the detection of crossing traffic were derived. These requirements were met by developing a video system based on a monocular wide field of view camera. It is further shown how this action concept and sensor were integrated into a demonstrator vehicle and evaluated in full scale testing.

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Section: A Collision Mitigation At Intersectionsmentioning

confidence: 99%

Collision mitigation for crossing traffic in urban scenarios

Heck

Bellin

Matousek

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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“…Prominent examples are parking assistance systems and night vision cameras. In the INTERSAFE project laser scanners are used to track and classify road users and obstacles, providing additional data for the path prediction and risk assessment [7]. Further systems, supporting the driver in an implicit way, include support for acceleration and breaking [24], as well as ABS / ESP.…”

Section: Sensors In Vehiclesmentioning

confidence: 99%

Enabling micro-entertainment in vehicles based on context information

Alt

Kern

Schulte

et al. 2010

Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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People spend a significant amount of time in their cars (US: 86 minutes/day, Europe: 43 minutes/day) while commuting, shopping, or traveling. Hence, the variety of entertainment in the car increases, and many vehicles are already equipped with displays, allowing for watching news, videos, accessing the Internet, or playing games. At the same time, the urbanization caused a massive increase of traffic volume, which led to people spending an ever-increasing amount of their time in front of red traffic lights. An observation of the prevailing forms of entertainment in the car reveals that content such as text, videos, or games are often a mere adaptation of content produced for television, public displays, PCs, or mobile phones and do not adapt to the situation in the car. In this paper we report on a web survey assessing which forms of entertainment and which types of content are considered to be useful for in-car entertainment by drivers. We then introduce an algorithm, which is capable of learning standing times in front of traffic lights based on GPS information only. This, on one hand, allows for providing content of appropriate length, on the other hand, for directing the attention of the driver back to-wards the street at the right time. Finally, we present a prototype implementation and a qualitative evaluation.

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“…The first has its origin in the area of autonomous vehicles and driver assistance systems, the second in automated highway systems. The third possibility is to combine both sources of information [11], which is our approach too.…”

Section: A Cooperative Collision Warningmentioning

confidence: 99%

“…The rest of this paper concentrates on the important task of detecting upcoming dangerous situations for a CG automatically. (20) with the weights defined as follows: (11) Wi(m) = 1/(2(L + A)) for i = 1, ... , 2L (12) The covariance matrix of x(t) is the weighted sum 2£ (13) We let x(t, i) be the propagation of x(to, i) through the differential equations (5) till time t for i = 0, ... , 2L. The differential equation can be solved in MATLAB with the ode45 function for non-stiff ODEs.…”

Section: Information Processing Cyclementioning

confidence: 99%

Recognition of dangerous situations within a cooperative group of vehicles

Batz

Watson

Beyerer

2009

2009 IEEE Intelligent Vehicles Symposium

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We consider the recognition of dangerous situations in vehicle traffic. Unscented Kalman filters are used to predict vehicle trajectories within a short prediction horizon [t(0), t(0) + Delta t]. Based on this prediction, for each vehicle pair the mutual distance is computed for [t(0), t(0) + Delta t], whereby the distance accounts for the geometric distance, for the prediction uncertainties as well as for the spatial dimensions of the vehicles. If at least one of the mutual distances falls below a distance threshold epsilon within [t(0), t(0) + Delta t], then a dangerous situation arises for the cooperative group and may lead to an autonomous cooperative driving manoeuvre. This approach allows the usage of the system in a mixed environment (only some vehicles are cooperative and cognitive). Obstacles can also be handled. The key issues in this ongoing research work are the recognition and classification of dangerous situations and the formation of a cooperative group constituting an operational unit. A common relevant picture within a group coordinator fuses the necessary information from all cooperative vehicles of the group and forms the basis for situation recognition and classification. This paper is a step to expand a Cooperative Collision Warning System (CCWS) to an integrated Cooperative Collision Avoidance and Cooperative Collision Mitigation System (CCAMS)

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Advanced Intersection Safety - The EC project INTERSAFE

Cited by 9 publications

References 2 publications

Collision mitigation for crossing traffic in urban scenarios

Collision mitigation for crossing traffic in urban scenarios

Enabling micro-entertainment in vehicles based on context information

Recognition of dangerous situations within a cooperative group of vehicles

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