Marc Necker

A Novel Algorithm for Distributed Dynamic Interference Coordination in Cellular Networks

Wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA) multiplex different users in time and frequency. One of the main problems in OFDMA-systems is the inter-cell interference. A promising approach to solve this problem is interference coordination (IFCO). In this paper, we present a novel distributed IFCO scheme, where a central coordinator communicates coordination information in regular time intervals. This information is the basis for a local inner optimization in every basestation. The proposed scheme achieves an increase of more than 100% with respect to the cell edge throughput, and a gain of about 30% in the aggregate spectral efficiency compared to a reuse 3 system.

Ego-lane estimation for lane-level navigation in urban scenarios

Future lane-precise navigation systems will recommend lane changes to drivers if needed. To achieve this, robust lane-level localization on a navigable map is essential. We propose an ego-lane estimation algorithm to robustly determine the ego-lane in urban scenarios based on a particle filter approach. The method only requires sensors available in a current production car, i.e. visual lane-marking detection, radar, and GPS, and a digital map describing road geometry and topology. Extensive experimental validation has shown an error rate of less than 0.75% with an availability of 95% of the total time and below 0.4% at 96% availability in situations most relevant for navigation. The influence of the used sensors has been evaluated.

Lane-level map-matching based on optimization

We propose a method for ego-lane estimation that can robustly determine the currently used lane as required by future lane-precise navigation systems. It employs a lane-level map-matching on a digital road map through least-squares optimization and only requires sensors available in current production vehicles, such as GPS, odometry, visual lane-marking detection and radars. Radar data is used in a RANSAC-based filtering step for lane hypotheses and, together with camera data, in the determination of the reliability of each lane hypothesis. Detailed evaluation in actual traffic in urban scenarios shows very low error rates below 0.2%.

Ego-lane estimation for downtown lane-level navigation

We present an ego-lane estimation algorithm for downtown lane-level navigation. It is capable of determining the currently used lane reliably, using sensors available in a modern production vehicle, such as odometry, GPS, visual lane-marking detection, and radar-based object detection. The method employs a particle filter with a novel step that combines the importance weight update and sampling. This step avoids performance deterioration in case of sparse particle sets even when the likelihood is very tight compared to the predicted particle set. Preprocessed odometry data allow for a further performance increase. In an extensive test in downtown scenarios on real roads with up to seven lanes, it achieves error probabilities below 1% in the 95th percentile at availabilities above 95%.