Autonomous Vehicle and Smart Traffic

Long-term forecasting of technology has become extremely difficult due to the rapid realization of any suggested idea. Communication and software technologies can compensate for the problems that may arise during the transition period between idea generation and realization. However, this rapid process can cause problems for the automotive industry and transportation systems.Autonomous vehicles are currently a hot topic within the transportation sector. This development is related to the compatibility of vehicles of the near future with the development of the infrastructure on which these vehicles will be based. There are certain problems regarding the solutions that are currently being worked on, such as how autonomous should vehicles be, their control mechanisms, driving safety, energy requirements, and environmental use. The problem is not just about the design of autonomous vehicles. The user transportation systems of these vehicles also need problem-free solutions. The problem should not only be seen as financial because sociological effects are an important part of this feature.In this book, valuable research on the modeling, systems, transportation, technological necessity, and logistics of autonomous vehicles is presented. The content of the book will help researchers to create ideas for their future studies and to open up the discussion of autonomous vehicles

A Deep Learning Approach for Spatiotemporal-Data-Driven Traffic State Estimation

The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed up to 60 minutes ahead. Att-MED was designed to encode multiple traffic input sequences: short-term, daily, and weekly cyclic behavior. The proposed network produced an average prediction accuracy of 97.5%, which was superior to the compared baseline models. In addition to improving the output performance, the model\u27s attention weights enhanced the model interpretability. This research additionally explored the utility of low-penetration connected probe-vehicle data for network-wide traffic parameters estimation and prediction on freeways. A novel sequence-to-sequence recurrent graph networks (Seq2Se2 GCN-LSTM) was designed. It was then trained to estimate and predict traffic volume and speed for a 60-minute future time horizon. The proposed methodology generated volume and speed predictions with an average accuracy of 90.5% and 96.6%, respectively, outperforming the investigated baseline models. The proposed method demonstrated robustness against perturbations caused by the probe vehicle fleet\u27s low penetration rate. Secondly, the application of deep learning for road weather detection using roadside CCTVs were investigated. A Vision Transformer (ViT) was trained for simultaneous rain and road surface condition classification. Next, a Spatial Self-Attention (SSA) network was designed to consume the individual detection results, interpret the spatial context, and modify the collective detection output accordingly. The sequential module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score, raising the total accuracy for both tasks to 96.71% and 98.07%, respectively. Thirdly, a real-time video-based traffic incident detection algorithm was developed to enhance the utilization of the existing roadside CCTV network. The methodology automatically identified the main road regions in video scenes and investigated static vehicles around those areas. The developed algorithm was evaluated using a dataset of roadside videos. The incidents were detected with 85.71% sensitivity and 11.10% false alarm rate with an average delay of 27.53 seconds. In general, the research proposed in this dissertation maximizes the utility of pre-existing traffic infrastructure and emerging probe traffic data. It additionally demonstrated deep learning algorithms\u27 capability of modeling complex spatiotemporal traffic data. This research illustrates that advances in the deep learning field continue to have a high applicability potential in the traffic state estimation domain

Wide area detection system: Conceptual design study

An integrated sensor for traffic surveillance on mainline sections of urban freeways is described. Applicable imaging and processor technology is surveyed and the functional requirements for the sensors and the conceptual design of the breadboard sensors are given. Parameters measured by the sensors include lane density, speed, and volume. The freeway image is also used for incident diagnosis

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

Motorcycle Conspicuity: The Effects Of Age And Vehicular Daytime Running Lights

Research has shown that riding a motorcycle can potentially be much more dangerous than operating a conventional vehicle. There are factors inherent in driving or riding a small two wheeled vehicle, such as a motorcycle, moped or even bicycle that can potentially decrease their ability to be seen or noticed by other drivers. This disadvantage is reflected in the disproportionate over-representation of injuries and/or fatalities incurred by this particular driving group. This creates a significant problem which deserves dedicated evaluation as to causative factors and/or influential variables. The following research was conducted with intentions to investigate the topic of motorcycle conspicuity so as to further explain the variables which positively contribute to a motorcycle being seen and to supplement the body of knowledge that currently exists on this topic. This study specifically evaluated the influence of sex, age, motorcycle lighting conditions, and vehicular daytime running lights upon one\u27s ability to effectively detect a motorcycle within a high fidelity simulated environment. This research additionally sought to examine the feasibility and validity of using a novel fixed base high fidelity simulator for the evaluation of motorcycle conspicuity. The results from this research clearly indicate a link between vehicular DRLs and the effective detection of motorcycles and also support previous research as to the effectiveness of motorcycle DRLs. Additionally, these results suggest that as one ages, certain degradations in vision, cognition, and physiology occur which decrease one\u27s performance in detecting and responding to a motorcycle. These findings additionally provide support for the use of a high definition fixed base simulator as a valid technology for the evaluation of motorcycle conspicuity

Automatic individual pig detection and tracking in pig farms

Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, due to problems of light fluctuation, similar appearances of pigs, shape deformations and occlusions. In order to tackle these problems, we propose a robust on-line multiple pig detection and tracking method which does not require manual marking or physical identification of the pigs, and works under both daylight and infrared (nighttime) light conditions. Our method couples a CNN-based detector and a correlation filter-based tracker via a novel hierarchical data association algorithm. In our method, the detector gains the best accuracy/speed trade-off by using the features derived from multiple layers at different scales in a one-stage prediction network. We define a tag-box for each pig as the tracking target, from which features with a more local scope are extracted for learning, and the multiple object tracking is conducted in a key-points tracking manner using learned correlation filters. Under challenging conditions, the tracking failures are modelled based on the relations between responses of detector and tracker, and the data association algorithm allows the detection hypotheses to be refined, meanwhile the drifted tracks can be corrected by probing the tracking failures followed by the re-initialization of tracking. As a result, the optimal tracklets can sequentially grow with on-line refined detections, and tracking fragments are correctly integrated into respective tracks while keeping the original identifications. Experiments with a dataset captured from a commercial farm show that our method can robustly detect and track multiple pigs under challenging conditions. The promising performance of the proposed method also demonstrates a feasibility of long-term individual pig tracking in a complex environment and thus promises a commercial potential

Standardization Roadmap for Unmanned Aircraft Systems, Version 1.0

This Standardization Roadmap for Unmanned Aircraft Systems, Version 1.0 (“roadmap”) represents the culmination of the UASSC’s work to identify existing standards and standards in development, assess gaps, and make recommendations for priority areas where there is a perceived need for additional standardization and/or pre-standardization R&D. The roadmap has examined 64 issue areas, identified a total of 60 gaps and corresponding recommendations across the topical areas of airworthiness; flight operations (both general concerns and application-specific ones including critical infrastructure inspections, commercial services, and public safety operations); and personnel training, qualifications, and certification. Of that total, 40 gaps/recommendations have been identified as high priority, 17 as medium priority, and 3 as low priority. A “gap” means no published standard or specification exists that covers the particular issue in question. In 36 cases, additional R&D is needed. The hope is that the roadmap will be broadly adopted by the standards community and that it will facilitate a more coherent and coordinated approach to the future development of standards for UAS. To that end, it is envisioned that the roadmap will be widely promoted and discussed over the course of the coming year, to assess progress on its implementation and to identify emerging issues that require further elaboration

Research and technology at the Lyndon B. Johnson Space Center

Johnson Space Center accomplishments in new and advanced concepts during 1983 are highlighted. Included are research funded by the Office of Aeronautics and Space Technology; Advanced Programs tasks funded by the Office of Space Flight; and Solar System Explorations, Life Sciences, and Earth Sciences and Applications research funded by the Office of Space Sciences and Applications. Summary sections describing the role of the Johnson Space Center in each program are followed by one-page descriptions of significant projects. Descriptions are suitable for external consumption, free of technical jargon, and illustrated to increase ease of comprehension