A model-based object following system

Müller, André C.; Manz, Markus G.; Himmelsbach, Michael; Wünsche, Hans-Joachim

doi:10.1109/ivs.2009.5164285

Cited by 12 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…an adaptive cruise control situation is driving on the highway while an autonomous system keeps the distance to the car in front. One or more sensors like RADAR, LIDAR or camera sensors are used to obtain information about the surrounding environment [1]- [3]. The measurement range goes from the 1D distance up to the complete 3D position, orientation and velocity.…”

Section: Introductionmentioning

confidence: 99%

Combining model- and template-based vehicle tracking for autonomous convoy driving

Fries

Luettel

Wuensche

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

This paper presents a robust method for vehicle tracking with a monocular camera. A previously published model-based tracking method uses a particle filter which needs an initial vehicle hypothesis both at system start and in case of a tracking loss. We present a template-based solution using different features to estimate a 3D vehicle pose roughly but fast. Combining model-and template-based object tracking keeps the advantages of each algorithm: Precise estimation of the 3D vehicle pose and velocity combined with a fast (re-) initialization approach. The improved tracking system was evaluated while driving autonomously in urban and unstructured environments. The results show that poorly visible vehicles can be tracked during different weather conditions in real-time.

show abstract

Section: Introductionmentioning

confidence: 99%

Combining model- and template-based vehicle tracking for autonomous convoy driving

Fries

Luettel

Wuensche

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Vision and radar are used in [15], whereas vision and lidar are used in [16]. In [17] lidar is used to detect the leading vehicle, and the movement of the leading vehicle is then used to estimate the lane and the driven path, which in turn is used to autonomously follow this vehicle. This works well even for curved and narrow roads.…”

Section: Introductionmentioning

confidence: 99%

Joint ego-motion and road geometry estimation

Lundquist

Schön

2011

Information Fusion

View full text Add to dashboard Cite

We provide a sensor fusion framework for solving the problem of joint ego-motion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.

show abstract