Contextual information aided target tracking and path planning for autonomous ground vehicles

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion

A review of advances in pixel detectors for experiments with high rate and radiation

The Large Hadron Collider (LHC) experiments ATLAS and CMS have established hybrid pixel detectors as the instrument of choice for particle tracking and vertexing in high rate and radiation environments, as they operate close to the LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for which the tracking detectors will be completely replaced, new generations of pixel detectors are being devised. They have to address enormous challenges in terms of data throughput and radiation levels, ionizing and non-ionizing, that harm the sensing and readout parts of pixel detectors alike. Advances in microelectronics and microprocessing technologies now enable large scale detector designs with unprecedented performance in measurement precision (space and time), radiation hard sensors and readout chips, hybridization techniques, lightweight supports, and fully monolithic approaches to meet these challenges. This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog. Phy

Signal and noise of Diamond Pixel Detectors at High Radiation Fluences

CVD diamond is an attractive material option for LHC vertex detectors because of its strong radiation-hardness causal to its large band gap and strong lattice. In particular, pixel detectors operating close to the interaction point profit from tiny leakage currents and small pixel capacitances of diamond resulting in low noise figures when compared to silicon. On the other hand, the charge signal from traversing high energy particles is smaller in diamond than in silicon by a factor of about 2.2. Therefore, a quantitative determination of the signal-to-noise ratio (S/N) of diamond in comparison with silicon at fluences in excess of 10$^{15}$ n$_{eq}$ cm$^{-2}$, which are expected for the LHC upgrade, is important. Based on measurements of irradiated diamond sensors and the FE-I4 pixel readout chip design, we determine the signal and the noise of diamond pixel detectors irradiated with high particle fluences. To characterize the effect of the radiation damage on the materials and the signal decrease, the change of the mean free path $\lambda_{e/h}$ of the charge carriers is determined as a function of irradiation fluence. We make use of the FE-I4 pixel chip developed for ATLAS upgrades to realistically estimate the expected noise figures: the expected leakage current at a given fluence is taken from calibrated calculations and the pixel capacitance is measured using a purposely developed chip (PixCap). We compare the resulting S/N figures with those for planar silicon pixel detectors using published charge loss measurements and the same extrapolation methods as for diamond. It is shown that the expected S/N of a diamond pixel detector with pixel pitches typical for LHC, exceeds that of planar silicon pixels at fluences beyond 10$^{15}$ particles cm$^{-2}$, the exact value only depending on the maximum operation voltage assumed for irradiated silicon pixel detectors

In vivo tracking and immunological properties of pulsed porcine monocyte-derived dendritic cells

Cellular therapies using immune cells and in particular dendritic cells (DCs) are being increasingly applied in clinical trials and vaccines. Their success partially depends on accurate delivery of cells to target organs or migration to lymph nodes. Delivery and subsequent migration of cells to regional lymph nodes is essential for effective stimulation of the immune system. Thus, the design of an optimal DC therapy would be improved by optimizing technologies for monitoring DC trafficking. Magnetic resonance imaging (MRI) represents a powerful tool for non-invasive imaging of DC migration in vivo. Domestic pigs share similarities with humans and represent an excellent animal model for immunological studies. The aim of this study was to investigate the possibility using pigs as models for DC tracking in vivo. Porcine monocyte derived DC (MoDC) culture with superparamagnetic iron oxide (SPIO) particles was standardized on the basis of SPIO concentration and culture viability. Phenotype, cytokine production and mixed lymphocyte reaction assay confirmed that porcine SPIO-MoDC culture were similar to mock MoDCs and fully functional in vivo. Alike, similar patterns were obtained in human MoDCs. After subcutaneous inoculation in pigs, porcine SPIO-MoDC migration to regional lymph nodes was detected by MRI and confirmed by Perls staining of draining lymph nodes. Moreover, after one dose of virus-like particles-pulsed MoDCs specific local and systemic responses were confirmed using ELISPOT IFN-γ in pigs. In summary, the results in this work showed that after one single subcutaneous dose of pulsed MoDCs, pigs were able to elicit specific local and systemic immune responses. Additionally, the dynamic imaging of MRI-based DC tracking was shown using SPIO particles. This proof-of-principle study shows the potential of using pigs as a suitable animal model to test DC trafficking with the aim of improving cellular therapies.We want to thank: Ferrán López, Rosa López, Zoraida Cervera, Pamela Martinez-Orellana, Tufaria Mussá, Massimiliano Baratelli, Diego Pérez, Sergio López from CRESA and José Luis Ruiz de la Torre and Javier Aceña (UAB) for farm and technical support; Jaume Martorell (Fundació Hospital Clínic Veterinari, UAB) for MRI support; Javier Domínguez (INIA) for the porcine antibodies; Antonio Lestuzzi, Michele Crisci and Raif Yucel for MR imaging support; Joaquim Segalés for anatomic pathology analysis; Mónica Pérez for immunohistochemical stainings; Aida Neira and Blanca Pérez for Perls staining; Eva Huerta y Marina Sibila for PCV2 PCR; David Andreu and Beatriz García de la Torre (Pompeu Fabra University, Barcelona), and Esther Blanco (CISA-INIA, Madrid), for the FMDV 3A peptide; Alicia Solórzano for critically reviewing the manuscript. This work was funded by the project AGL2010-22200-C02 of Spanish Ministry of Science and Innovation. PhD studies of Raquel Cabezón are funded by a doctoral FI fellowship from the Generalitat de Catalunya

A Comprehensive Mapping and Real-World Evaluation of Multi-Object Tracking on Automated Vehicles

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field. This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of MOTs and various MOT subfields for AVs that have not been presented as wholistically in other papers. The second contribution aims to illustrate some of the benefits of using a COTS MOT toolset and some of the difficulties associated with using real-world data. This MOT performed accurate state estimation of a target vehicle through the tracking and fusion of data from a radar and vision sensor using a Central-Level Track Processing approach and a Global Nearest Neighbors assignment algorithm. It had an 0.44 m positional Root Mean Squared Error (RMSE) over a 40 m approach test. It is the authors\u27 hope that this work provides an overview of the MOT field that will help new researchers and practitioners enter the field. Additionally, the author hopes that the evaluation section illustrates some difficulties of using real-world data and provides a good pathway for developing and deploying MOTs from software toolsets to Automated Vehicles

Multi-Base Station Cooperative Sensing with AI-Aided Tracking

In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m