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© 2015 IEEE.Due to increasing sensor resolutions, the commonly used point-target assumption in multi-object tracking algorithms is violated. Recently, several algorithm based on Gaussian inverse Wishart (GIW) or Gamma GIW (GGIW) distributions have been proposed which facilitate the tracking of so-called extended targets which generate multiple measurements per scan. Using GGIW distributions, the target extent and the measurement rate are estimated in addition to the targets' kinematics. In this contribution, the GGIW Labeled Multi-Bernoulli (GGIW-LMB) filter is applied to a scenario with a huge amount of clutter measurements. Additionally, three different target birth models are compared for different clutter rates: static birth distributions, adaptive birth distributions, and adaptive two-step distributions.

Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended target tracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.

Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer. © 2014 SPIE.

This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the $delta$-Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the $delta$ -Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance.

In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, the labeled multi-Bernoulli filter is not prone to the biased cardinality estimate of the multi-Bernoulli filter. The utilization of the class of labeled random finite sets naturally incorporates the estimation of a targets identity or label. Compared to the d-generalized labeled multi-Bernoulli filter, the labeled multi-Bernoulli filter is anefficient approximation which obtains almost the same accuracy at significantly lower computational cost. The performance of thelabeled multi-Bernoulli filter is compared to the multi-Bernoulli filter using simulated data. Further, the real-time capability of the filter is illustrated using real-world sensor data of our experimental vehicle.

This paper discusses the design of an Advanced Driver Assistance System (ADAS) that ensures safety when traffic rules are respected by all road users. This concept, referred to as legal safety, is proposed as a basis that permits human and automated drivers to share the road infrastructure. It is illustrated for a Highly Automated driving System with speed keeping, distance keeping and lane changing functionalities on highways (HAS). The requirements legal safety places upon HAS components are presented, with a special focus on the co-pilot which calculates a safe trajectory for the vehicle based on perception of lanes and traffic signs and prediction of the trajectories of objects in the environment. A lane coordinate system is proposed as a powerful reference for the trajectory calculations of the co-pilot. The system controls the vehicle and communicates with the driver, according to an automation mode scheme inspired by the horse-rider metaphor (H-metaphor).

Inter-Vehicular Communications (IVC) are considered a promising technological approach for enhancing transportation safety and improving highway efficiency. Previous theoretical work has demonstrated the benefits of IVC in vehicles strings. Simulations of partially IVC-equipped vehicles strings showed that only a small equipment ratio is sufficient to drastically reduce the number of head on collisions. However, these results are based on the assumptions that IVC exhibit lossless and instantaneous messages transmission. This paper presents the research design of an empirical measurement of a vehicles string, with the goal of highlighting the constraints introduced by the actual characteristics of communication devices. A warning message diffusion system based on IEEE 802.11 wireless technology was developed for an emergency breaking scenario. Preliminary results are presented as well, showing the latencies introduced by using 802.11a and discussing early findings and experimental limitations