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Autonomous driving poses unique challenges for vehicle environment perception due to the complicated driving environment where the autonomous vehicle connects itself with surrounding objects. Precise tracking of the relevant dynamic traffic participants (e.g., vehicle/byciclist/pedestrian) becomes a key component for the task of comprehensive environmental perception and reliable scene understanding...
We introduce a novel pose graph-based localization technique for autonomous driving that incorporates both sparse point features as well as lane markings on the road. Unlike many commonly used filter methods, our graph-based localization takes a much larger history of the trajectory into account. In addition, we utilize a multiple-hypothesis approach for data association for increased robustness against...
An important requirement in autonomous driving for many complex scenarios is to correctly detect static and dynamic targets under various states of motion. The possibility of fulfilling this requirement depends upon the availability of different sensor data to the sensor fusion module. This paper uses data from sensors with built-in tracking modules and our objective is to make the resultant of two...
The detection and tracking of extended targets is a challenging problem. The accuracy of the detections and the tracking is dependent on the granularity and the quality of the data obtained from the sensors. The systems specialised for automotive environment perception task are mostly a mix of high and low resolution sensors, equipped with their own target state estimation module. The sensors provide...
Autonomous driving poses unique challenges for vehicle environment perception due to the complex surrounding environment of random and dynamic nature. An autonomous vehicle uses a variety of sensors such as radars, cameras and lidars to obtain the reliable and accurate information on the surrounding environment using a sensor fusion procedure. Each sensor processes data using a local tracker and a...
When fusing data from more than one information source, it is important to associate the correct pair of the data available from the information sources to achieve an optimal fused result. The responsibility of the task of proper association lies on the data association method used in the system. The design of the data association method has to be done considering the requirements of the application,...
The information fusion of the processed sensory tracks is carried out using track-to-track fusion algorithms. The performance analysis of a selected track-to-track fusion algorithms under different sensory track covariance configurations are carried out in this paper. This is the first paper that does the study on the influence of sensory track covariance on the performance of three important algorithms...
This paper presents the problem of information fusion in a multi-sensor setup of asynchronous sensors with different latencies. This leads to the problem of tracks that have arbitrary arrival time at the fusion center. A solution for the integration of tracks that are temporally out of order is proposed. The proposed algorithm is quite suitable for the trackto-track fusion requirements. This solution...
The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided...
Fusion of information from different sensor systems is vital for automotive safety systems. In a typical automotive sensor fusion setup the fusion can be a measurement fusion or a track level fusion in a centralized fusion center. Track level fusion is desired due to communication, computation and organizational constraints. Track level fusion algorithms have to deal with different correlation issues...
A new approach to precise timestamping and temporal synchronization in a multi-sensor setup is presented. Modern driver assistance systems use an increasing amount of different sensors that do not provide the exact timestamps of the measurements, nor the period of time between two measurements. This paper describes a method to determine these timestamps up to millisecond accuracy. Possible drifts...
This paper presents a new approach to exact timestamping of asynchronous measurements in a multi-sensor setup. In order to improve the performance of a single sensor, driver assistance systems use several different sensors which have different latencies that usually cannot be measured directly. These unknown latencies pose a problem in data association and temporal synchronization. Consequently, a...
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