DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks

Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause. This paper presents a method for managing nonuniformities and uncertainties found on datasets, based on an elaborate Matrix Completion technique, with superior performance in three distinct cases of vehicle-related sensor data, collected under real driving conditions. Our approach appears capable of handling sensing and communication irregularities, minimizing at the same time the storage and transmission requirements of Multi-access Edge Computing applications

Efficient moving point handling for incremental 3D manifold reconstruction

As incremental Structure from Motion algorithms become effective, a good sparse point cloud representing the map of the scene becomes available frame-by-frame. From the 3D Delaunay triangulation of these points, state-of-the-art algorithms build a manifold rough model of the scene. These algorithms integrate incrementally new points to the 3D reconstruction only if their position estimate does not change. Indeed, whenever a point moves in a 3D Delaunay triangulation, for instance because its estimation gets refined, a set of tetrahedra have to be removed and replaced with new ones to maintain the Delaunay property; the management of the manifold reconstruction becomes thus complex and it entails a potentially big overhead. In this paper we investigate different approaches and we propose an efficient policy to deal with moving points in the manifold estimation process. We tested our approach with four sequences of the KITTI dataset and we show the effectiveness of our proposal in comparison with state-of-the-art approaches.Comment: Accepted in International Conference on Image Analysis and Processing (ICIAP 2015

Initial planetary base construction techniques and machine implementation

Conceptual designs of (1) initial planetary base structures, and (2) an unmanned machine to perform the construction of these structures using materials local to the planet are presented. Rock melting is suggested as a possible technique to be used by the machine in fabricating roads, platforms, and interlocking bricks. Identification of problem areas in machine design and materials processing is accomplished. The feasibility of the designs is contingent upon favorable results of an analysis of the engineering behavior of the product materials. The analysis requires knowledge of several parameters for solution of the constitutive equations of the theory of elasticity. An initial collection of these parameters is presented which helps to define research needed to perform a realistic feasibility study. A qualitative approach to estimating power and mass lift requirements for the proposed machine is used which employs specifications of currently available equipment. An initial, unmanned mission scenario is discussed with emphasis on identifying uncompleted tasks and suggesting design considerations for vehicles and primitive structures which use the products of the machine processing

Anomaly Detection with Model Contradictions for Autonomous Driving

Anomaly detection is a critical aspect of safe autonomous driving systems, where detecting and understanding uncommon and unpredictable scenarios, often referred to as corner cases or anomalies, is crucial for ensuring the safety of passengers and pedestrians. In this bachelor\u27s thesis, I quantitatively evaluate an anomaly detection method proposed by Sartoris that utilizes lidar data for detecting anomalies. The method combines a supervised and a self-supervised part to detect motion anomalies in the environment. By analyzing the discrepancies between the two parts, the detection method identifies points that deviate from the expected behavior, indicating potential anomalies. This evaluation utilizes the CODA dataset, which provides the only anomaly dataset including lidar data. However, the corner cases are only labeled in the form of 2D bounding boxes. To address this limitation, I convert the 2D bounding boxes in the CODA dataset into 3D point-wise labels. The CODA dataset is then translated into the KITTI-odometry data format suitable for evaluating Sartoris\u27 method. Additionally, improvements are proposed for the clustering algorithms used to create 3D point-wise labels, aiming to reduce the need for manual verification. Given the lack of suitable metrics for semantic segmentation, except for IoU, I propose two novel approaches that utilize the metrics AP, AR, and F1 to quantitatively evaluate Sartoris\u27 anomaly detection approach on the CODA dataset. The results demonstrate the potential of this method in detecting anomalies compared to standard object detection techniques carried out by Li et al., despite the challenge of comparing my metrics with theirs. In the outlook of this thesis, the potential extensions and improvements to the detection approach are discussed, such as fine-tuning the models for the original datasets and addressing challenging scenarios like fast turns or speed bumps. Furthermore, the need for appropriate metric

State of the Art of Magnetic Gears, their Design, and Characteristics with Respect to EV Application

This chapter briefly explains the advantage of using magnetic gears (MGs) for transportation applications. Usually, a traction EV unit consists of, besides the engine or motor, a mechanical gear. The drawbacks of using mechanical gears have been emphasized, especially with respect to high-speed motorization, where high transmission ratio can be reached only by connecting multiple gears in series. A magnetic gear is capable of overcoming these issues. The chapter presents a state of the art on the available MGs, with fixed or variable transmission ratio, pointing out their applicability. Next, the possible design approaches (harmonic, magnetic reluctance equivalent circuit, and vector potential) are introduced. Furthermore, the output performances (power and torque) of two types of studied MGs are evaluated, with emphasis on the main loss criteria: iron losses in all the active parts of the MG. Finally, the influence of several materials is observed by means of numerical computation in order to decide, based on specific configuration, the most suited variant for transportation and aeronautic applications