Knowledge-Based Route Planning Through Natural Terrain

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.

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.

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

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.

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.

In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. We developed a mathematical model based on these properties, that was NP-hard. Then we proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. The numerical results show the operational cost in the mixed fleet decreases on average by 57.22% compared with the homogeneous fleet.

This article describes overview of automated and, in perspective, autonomous vehicles’ (AV) implementation risks. Set of activities, actual before the use of AVs on public roads, minimizing negative technical and social problems of AVs’ implementation is presented. Classification of vehicle’s automated control systems operating conditions is formulated. Groups of tests for AVs are developed and justified, sequence of AVs’ testing system formation is proposed.

Autonomous vehicles are becoming ever more common and offer many attractive benefits to society. They can operate for long periods of time unattended, operate in environments that may be dangerous to humans, perform time consuming or repetitive tasks and all with greater efficiency and lower costs than humans. For these vehicles to be able to do these things, algorithms need to be designed and optimized that allow them to interact with the real-world environment in safe, effective, and efficient ways. We designed and built a set of three homogeneous water-based autonomous surface vehicles equipped with appropriate sensors and communications ability along with algorithms designed to allow these vehicles to perform various cooperative tasks using data obtained from the vehicles’ sensors and data shared between the vehicles. These vehicles were designed to be modular, economical, and, where possible, were constructed using off-the-shelf technology with programming designed to take advantage of these systems. When the COVID-19 pandemic put an end to lab and field work the physical vehicles were stored but the research continued utilizing a hybrid hardware-software simulation of the system. Three microcontrollers identical to the devices controlling the physical boats were attached via a Universal Serial Bus (USB) hub to a desktop computer running a simulated environment written in Python™. The three vehicles (microcontrollers) were given tasks including patrolling adjoining areas of the water body delineated by latitude and longitude boundaries while staying within their own boundary and avoiding collisions with the other vehicles. Initial testing was successful with the algorithm able to maintain the vehicles within their boundary >=95% of the time with no collisions. Additional problem types including parallel travel; wind and current challenges; and gradient tracking and relevant algorithms are discussed.

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.

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.

Recently, technological advancements in autonomous vehicle development have continued to accelerate. In this paper, we propose a lateral controller and navigation algorithm for autonomous vehicles based on the Differential Global Positioning System (DGPS), core technology of autonomous vehicles. In this study, an H-infinity (H∞) controller was designed lateral controller to ensure nominal safety and robustness against noise, external disturbances and structural and non-structural errors in vehicle modeling. Meanwhile, the “pure pursuit” approach which geometrically follows a path was employed as a path-tracking technique for the proposed navigation algorithm. A vehicle modeling simulation was conducted to evaluate the performance of the proposed method using a twodegrees- of-freedom linear model. The lateral H∞ controller and navigation algorithm were applied to an actual vehicle, Their performances were evaluated by comparing the path tracking error at various speeds of autonomous vehicles with that of the proportional-integral-derivative (PID) controller.

The recent progress in autonomous vehicle research and development has led to increasingly widespread testing of fully autonomous vehicles on public roads, where complex traffic scenarios arise. Along with these vehicles, partially autonomous vehicles, manually-driven vehicles, pedestrians, cyclists, and some animals can be present on the road, to which autonomous vehicles must react. This study focuses on a comprehensive survey of the literature on motion prediction and state estimation of vehicles and VRUs, which are essential for path planning and navigation functionalities of an autonomous vehicle. Motion prediction and state estimation methods utilize the vehicle’s own sensory perception capabilities and information obtained through cooperative perception from V2V and V2X connections. This survey summarizes the significant progress that has been made in both categories, discusses the most promising results to date and outlines critical research challenges that need to be overcome to achieve full autonomy, from an ego vehicle’s perspective in mixed traffic environments.

Our paper bases its research on the subject of deep learning to detect and predict braking events in Autonomous Vehicles. We used a Gradient Boosting (XGBoost) which is trained on data from IMU and GPS sensors from multiple cars. The model achieved high accuracy of 82.48% in classification of braking events by making use of features that were derived using vehicle kinematics. This intelligent framework surpassed old heuristic algorithms, and has a robust and extensible answer to a highly significant AV challenges. This research aims in helping the development of autonomous vehicles by advancing the perception and control system by means of accurate breaking identification method

The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future. The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system (ITS). The fundamental objectives of this work are to manage autonomous vehicles crossing an intersection with no collisions, maintaining that a vehicle drives continuously, and to decrease the waiting time at an intersection. In this paper, a discrete model of the one-way single intersection is designed. The vehicle-to-infrastructure (V2I) communication is implemented to exchange information between a vehicle and an intersection manager which is the roadside infrastructure. The safe trajectory of autonomous vehicles for the autonomous intersection management is determined and presented by using discrete mathematics.