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Road accidents remain as one of the major causes of death and injuries globally. Several million people die every year due to road accidents all over the world. Although the number of accidents in European region have reduced in the past years, road safety still remains a major challenge. Especially in case of commercial trucks, due to the size and load of the vehicle, even minor collisions with other road users would lead to serious injuries or death. In order to reduce number of accidents, automotive industry is rapidly developing advanced driver assistance systems (ADAS) and automated driving technologies. Efficient and reliable solutions are required for these systems to sense, perceive and react to different environmental conditions. For vehicle safety applications such as collision avoidance with vulnerable road users (VRUs), it is not only important for the system to efficiently detect and track the objects in the vicinity of the vehicle but should also function robustly.
An environment perception solution for application in commercial truck safety systems and for future automated driving is developed in this work. Thereby a method for integrated tracking and classification of road users in the near vicinity of the vehicle is formulated. The drawbacks in conventional multi-object tracking algorithms with respect to state, measurement and data association uncertainties have been addressed with the recent advancements in the field of unified multi-object tracking solutions based on random finite sets (RFS). Gaussian mixture implementation of the recently developed labeled multi-Bernoulli (LMB) filter [RSD15] is used as the basis for multi-object tracking in this work. Measurement from an high-resolution radar sensor is used as the main input for detecting and tracking objects.
On one side, the focus of this work is on tracking VRUs in the near vicinity of the truck. As it is beneficial for most of the vehicle safety systems to also know the category that the object belongs to, the focus on the other side is also to classify the road users. All the radar detections believed to originate from a single object are clustered together with help of density based spatial clustering for application with noise (DBSCAN) algorithm. Each cluster of detections would have different properties based on the respective object characteristics. Sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A machine learning based classifier is constructed, trained and parameterised for distinguishing the road users based on the extracted features.
The class information derived from the radar detections can further be used by the tracking algorithm, to adapt the model parameters used for precisely predicting the object motion according to the category of the object. Multiple model labeled multi-Bernoulli filter (MMLMB) is used for modelling different object motions. Apart from the detection level, the estimated state of an object on the tracking level also provides information about the object class. Both these informations are fused using Dempster-Shafer theory (DST) of evidence, based on respective class probabilities Thereby, the output of the integrated tracking and classification with MMLMB filter are classified tracks that can be used by truck safety applications with better reliability.
The developed environment perception method is further implemented as a real-time prototypical system on a commercial truck. The performance of the tracking and classification approaches are evaluated with the help of simulation and multiple test scenarios. A comparison of the developed approaches to a conventional converted measurements Kalman filter with global nearest neighbour association (CMKF-GNN) shows significant advantages in the overall accuracy and performance.
Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis.
This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals.
I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115
people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.