TOF Lidar Development in Autonomous Vehicle

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4× improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.

In this novel work the EMI shielding properties of low density, low cost and less explored carbon nano fiber (CNF) nanofiller embedded in the epoxy matrix is investigated for its potential use in electromagnetic interference (EMI) shielding of sensitive electronic equipment's applied in industrial, military applications and also in protection of human health. The CNF nanocomposite with filler concentrations 1, 5, 10 and 15 wt.% epoxy are synthesized via ultrasonication method. The EMI shielding efficiency of all the nanocomposites prepared are investigated in the X-band using VNA and the result shows that nanocomposite with CNF as 15 wt. % of epoxy achieved maximum EMI shielding efficiency of 35.53 dB in X-band at 2 mm thickness. Hence this lightweight CNF based nanocomposite can be potentially applied in many application such as military, industrial and civil application, where high shielding efficiency is required.

The utilization of Autonomous vehicles has been rapidly increasing in both civilian and military applications. This is primarily due to their exceptional communication capabilities with ground clients, as well as their inherent properties such as adaptability, mobility, and flexibility. To effectively monitor and control the deployment of Autonomous vehicles, a 5G wireless network can be utilized alongside other devices as user equipment (UE). Through the utilization of a highly directive microstrip patch antenna (MPA) functioning within a dedicated frequency band, the Autonomous vehicle is able to establish effective long-range communication, overcoming signal degradation and minimizing interference from other channels within a confined area. The adoption of MPA is especially advised for Unmanned Aerial Vehicles (UAVs) due to its characteristics such as being lightweight, cost-efficient, small in size, and having a flat structure. This study details the development and simulation of a highly directive single-band 1x2 and 1x4 antenna array designed to operate at a 5.8 GHz frequency specifically tailored for Autonomous vehicle use. To enhance directivity and ensure structural robustness, the Roger RT5880 material has been utilized as a substrate, known for its lower dielectric constant.. The results demonstrate a high gain of 9.16 dBi and 11.4 dBi for the 1x2 and 1x4 antenna arrays respectively, while maintaining a compact size. The simulated 1x4 antenna array was fabricated using the Roger RT5880 material, and this array was tested in the microwave communication's laboratory at College of Engineering, Al-Nahrain University. The antenna presents good results by means of return losses (-26.672 dB) which equivalent to VSWR of 1.098.

Greater prosociality toward other human drivers than autonomous vehicles: Human drivers’ discriminatory behavior in mixed traffic

The growing use of drones in civilian and military applications is motivated mainly by reduced costs. Drones, along with autonomous vehicles, require lightweight structures and high dimensional accuracy to improve autonomy and spatial orientation. Additive Manufacturing (AM) enables the production of lightweight parts by controlling internal fill percentages and optimizing the density. However, there is limited research into how the type and percentage of infill affects dimensional tolerance. This study aims to assess the impact of these factors on 3D-printed parts, using low-cost FFF 3D printers with Polylactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS) filaments. Overall, the results were satisfactory and met the required tolerances, confirming the feasibility of using parts produced by low-cost 3D printers and commonly available materials in the mass production of drones and autonomous vehicles.

Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to use two-dimensional, image-like projections. In this work, we perform a comprehensive experimental study of image-based semantic segmentation architectures for LiDAR point clouds. We demonstrate various techniques to boost the performance and to improve runtime as well as memory constraints. First, we examine the effect of network size and suggest that much faster inference times can be achieved at a very low cost to accuracy. Next, we introduce an improved point cloud projection technique that does not suffer from systematic occlusions. We use a cyclic padding mechanism that provides context at the horizontal field-of-view boundaries. In a third part, we perform experiments with a soft Dice loss function that directly optimizes for the intersection-over-union metric. Finally, we propose a new kind of convolution layer with a reduced amount of weight-sharing along one of the two spatial dimensions, addressing the large difference in appearance along the vertical axis of a LiDAR scan. We propose a final set of the above methods with which the model achieves an increase of 3.2% in mIoU segmentation performance over the baseline while requiring only 42% of the original inference time.

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.

The attitudes of the public play a critical role in the acceptance, purchase, utilization, and research and development of autonomous vehicles (AVs). Currently, the attitudes of the public toward AVs have been mostly estimated through traditional survey data, which bears a low quantity of samples with high labor costs. It is probably also one of the reasons why the critical factors on the attitudes of the public toward AVs have not been studied from a comprehensive perspective yet. To address the issue, this study aims to propose a method by using large-scale social media data to investigate key factors that affect the attitudes of the public toward AVs. A total of 954,151 Twitter data related to AVs and 53 candidate independent variables from seven categories were extracted using the web scraping method. Then, sentiment analysis was used to measure the public attitudes towards AVs by calculating sentiment scores. Random forests algorithm was employed to preliminarily select candidate independent variables according to their importance and a linear mixed model was utilized to explore the impacting factors, considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the attitudes of the public toward AVs were slightly optimistic. Factors, such as “drunk”, “blind spot”, and “mobility”, had the largest impacts on public attitudes. In addition, people were more likely to express positive feelings when talking about words, such as “lidar” and “Tesla”, related to high technologies. Conversely, factors, such as “COVID-19”, “pedestrian”, “sleepy”, and “highway”, were found to have significantly negative effects on the attitudes of the public. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the understanding and acceptance of the public toward AVs.

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples. Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3]. For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force. Civilian applications for such a device entail the investigation of hazardous areas (e.g. nuclear reactors and toxic waste dumps), search over large areas (e.g. downed aircraft or lost campers) and monitoring of large facilities (e.g. robot security police). As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature. The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action. This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter program.System OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage. Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate.

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability.

Recently, with the rapid development of intelligent autonomous vehicles (AVs), the safety of AVs become a critical concern for public consumers. Traditional safety evaluations for AVs focus on discovering unsafe cases during testing, however, they still leave several challenges especially in providing a safeguard of AVs acceptable to public consumers. First, existing schemes are unable to provide flexible public safety services for AVs, especially for customized safety services based on the demands of various consumers and traffic scenarios. Second, existing schemes cannot evaluate all traffic scenarios during testing such as future unknown scenarios and the scenarios with the degraded performance of the AVs. To address the aforementioned challenges, we propose a consumer-customized safety evaluation as a service for AVs, CSEaaS, which is a virtual-reality approach to provide scenario-coverable intelligent evaluating for AVs based on edge-cloud cooperation. Specifically, we design customizable safety indicators for AVs to satisfy the safety demands of consumers and construct an intelligent evaluating scheme based on virtual reality to test unknown traffic scenarios under real AV performance during traveling. Simulation results demonstrate the feasibility and effectiveness of CSEaaS. To the best of our knowledge, this is the first work to propose a customized evaluation-as-a-service for AVs.

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.

The advent of advanced computer architectures for parallel and symbolic processing has evolved to the point where the technology currently exists for the development of prototype autonomous vehicles. Control of such devices will require communication between knowledge-based subsystems in charge of the vision, planning, and conflict resolution aspects necessary to make autonomous vehicles functional in a real world environment. This paper describes a heuristic route planning system capable of forming the planning foundation of a autonomous ground vehicle. The route planner described herein is applicable to a variety of natural terrain ground systems such as autonomous tactical vehicles and mobile robot sentries. The route planner discussed consists of four processing stages: (1) scene recognition, (2) route planning, (3) scene matching (4) knowledge-based validation. Each of these stages is discussed and examples are provided where appropriate.

This paper seeks to evaluate the performance of the state of the art object classification algorithms for the purpose of cyclist detection using the Tsinghua–Daimler Cyclist Benchmark. This model focuses on detecting cyclists on the road for its use in development of autonomous road vehicles and advanced driver-assistance systems for hybrid vehicles. The Tiny YOLO v2 algorithm is used here and requires less computational resources and higher real-time performance than the YOLO method, which is extremely desirable for the convenience of such autonomous vehicles. The model has been trained using the training images in the mentioned benchmark and has been tested for the test images available for the same. The average IoU for all the truth objects is calculated and the precision-recall graph for different thresholds was plotted.

The advent of advanced computer architectures for parallel and symbolic processing has evolved to the point where the technology currently exists for the development of prototype autonomous vehicles. Control of such devices will require communication between knowledge-based subsystems in charge of the vision, planning, and conflict resolution aspects necessary to make autonomous vehicles functional in a real world environment. This paper describes a heuristic route planning system capable of forming the planning foundation of an autonomous ground vehicle.