Perceiving the dynamics of moving objects in complex scenarios is crucial for smart monitoring and safe navigation, thus a key enabler for intelligent supervision and autonomous driving. A variety of research has been developed to detect and track moving objects from data collected by optical sensors and/or laser scanners while most of them concentrate on certain type of objects or face the problem of lacking motion cues. In this thesis, we present a data-driven, model-free detection-based tracking approach for tracking moving objects in urban scenes from time sequential point clouds obtained via state-of-art Doppler LiDAR, which can not only collect spatial information (e.g. point clouds) but also Doppler images by using Doppler-shifted frequencies. In our approach, we first use Doppler images to detect moving points and determine the number of moving objects, which are then completely segmented via a region growing technique. The detected objects are then input to the tracking session which is based on Multiple Hypothesis Tracking (MHT) with two innovative extensions. One extension is that a new point cloud descriptor, <i>Oriented Ensemble of Shape Function (OESF)</i>, is proposed to evaluate the structure similarity when doing object-to-track association in MHT. Another extension is that speed information from Doppler images is used to predict the dynamic state of the moving objects, which is integrated into MHT to improve the estimation of dynamic state of moving objects. The proposed approach has been tested on datasets collected by a terrestrial Doppler LiDAR and a mobile Doppler LiDAR <a>separately</a>. The quantitative evaluation of detection and tracking results shows the unique advantages of the Doppler LiDAR and the effectiveness of the proposed detection and tracking approach.<br>

Indianapolis / E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.

E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.<br>

<p>From idea generation to conceptualization and development of products and technologies is a non-linear and iterative process. The work in this thesis follows a process that initiates with the review of existing technologies and products, examining their unique value proposition in the context of the specific applications for which they are designed. Next, the unmet needs of novel or emerging applications are identified that require new product or technologies. Once these user needs and product requirements are identified, the specific functions to be addressed by the product are specified. The subsequent process of design of products and technologies to meet these functions is enabled by engineering tools such as three-dimensional modelling, physics-based simulations, and manufacturing of a minimum viable prototype. In these steps, un-biased decisions have to be taken using weighted decision matrices to cater to the design requirements. Finally, the minimum viable prototype is tested to demonstrate the principal functionalities. The results obtained from the testing process identify the potential future improvements in the next generations of the prototype that would subsequently inform the final design of product. This thesis adopted this methodology to initiate the design two product-prototypes: i) an image-recognition-integrated service (IRIS) robotic hand for children and ii) cascaded multi-core vapor chamber (CMVC) for improving performance of next-generation computing systems. Minimum viable product-prototypes were manufactured to demonstrate the principal functionalities, followed by clear identification of future potential improvements. Tests of the prosthetic hand indicate that the image-recognition based feedback can successfully drive the actuators to perform the intended grasping motions. Experimental testing with the multi-core vapor chamber demonstrates successful performance of the prototype, which offers notable reduction in temperatures relative to the existing benchmark solid copper spreader. </p>