scholarly journals An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 120
Author(s):  
Pangwei Wang ◽  
Yunfeng Wang ◽  
Xu Wang ◽  
Ying Liu ◽  
Juan Zhang
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Inertial Navigation System ◽  
Route Planning ◽  
Autonomous Driving ◽  
Transportation Systems ◽  
A Algorithm ◽  
Logistics System ◽  
Planning Strategy ◽  
Logistics Distribution

Integration technologies of artificial intelligence (AI) and autonomous vehicles play important roles in intelligent transportation systems (ITS). In order to achieve better logistics distribution efficiency, this paper proposes an intelligent actuator of an indoor logistics system by fusing multiple involved sensors. Firstly, an actuator based on a four-wheel differential chassis is equipped with sensors, including an RGB camera, a lidar and an indoor inertial navigation system, by which autonomous driving can be realized. Secondly, cross-floor positioning can be realized by multi-node simultaneous localization and mappings (SLAM) based on the Cartographer algorithm Thirdly the actuator can communicate with elevators and take the elevator to the designated delivery floor. Finally, a novel indoor route planning strategy is designed based on an A* algorithm and genetic algorithm (GA) and an actual building is tested as a scenario. The experimental results have shown that the actuator can model the indoor mapping and develop the optimal route effectively. At the same time, the actuator displays its superiority in detecting the dynamic obstacles and actively avoiding the collision in the indoor scenario. Through communicating with indoor elevators, the final delivery task can be completed accurately by autonomous driving.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

scholarly journals Research and Educational Programs for Connected and Autonomous Vehicles at the University of Waterloo

2017 ◽  
Vol 139 (12) ◽  
pp. S21-S23
Author(s):  
Ross Mckenzie ◽  
John Mcphee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Engineering Students ◽  
Pattern Analysis ◽  
Autonomous Driving ◽  
Educational Programs ◽  
Transportation Systems ◽  
Systems Research ◽  
Vehicle Systems ◽  
The University

This article presents an overview of the research and educational programs for connected and autonomous vehicles at the University of Waterloo (UWaterloo). UWaterloo is Canada’s largest engineering school, with 9,500 engineering students and 309 engineering faculty. The University of Waterloo Centre for Automotive Research (WatCAR) for faculty, staff and students is contributing to the development of in-vehicle systems education programs for connected and autonomous vehicles (CAVs) at Waterloo. Over 130 Waterloo faculty, 110 from engineering, are engaged in WatCAR’s automotive and transportation systems research programs. The school’s CAV efforts leverage WatCAR research expertise from five areas: (1) Connected and Autonomous; (2) Software and Data; (3) Lightweighting and Fabrication; (4) Structure and Safety; and (5) Advanced Powertrain and Emissions. Foundational and operational artificial intelligence expertise from the University of Waterloo Artificial Intelligence Institute complements the autonomous driving efforts, in disciplines that include neural networks, pattern analysis and machine learning.

scholarly journals How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6306 ◽  
Author(s):  
Luke Butler ◽  
Tan Yigitcanlar ◽  
Alexander Paz
Keyword(s):  
Conceptual Framework ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Technological Innovations ◽  
Potential Solution ◽  
Wide Range ◽  
Smart Mobility

Transportation disadvantage is about the difficulty accessing mobility services required to complete activities associated with employment, shopping, business, essential needs, and recreation. Technological innovations in the field of smart mobility have been identified as a potential solution to help individuals overcome issues associated with transportation disadvantage. This paper aims to provide a consolidated understanding on how smart mobility innovations can contribute to alleviate transportation disadvantage. A systematic literature review is completed, and a conceptual framework is developed to provide the required information to address transportation disadvantage. The results are categorized under the physical, economic, spatial, temporal, psychological, information, and institutional dimensions of transportation disadvantage. The study findings reveal that: (a) Primary smart mobility innovations identified in the literature are demand responsive transportation, shared transportation, intelligent transportation systems, electric mobility, autonomous vehicles, and Mobility-as-a-Services. (b) Smart mobility innovations could benefit urban areas by improving accessibility, efficiency, coverage, flexibility, safety, and the overall integration of the transportation system. (c) Smart mobility innovations have the potential to contribute to the alleviation of transportation disadvantage. (d) Mobility-as-a-Service has high potential to alleviate transportation disadvantage primarily due to its ability to integrate a wide-range of services.

scholarly journals DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Fan Zhang ◽  
Jiaxing Luan ◽  
Zhichao Xu ◽  
Wei Chen
Keyword(s):  
Intelligent Transportation Systems ◽  
Research Effort ◽  
Recognition Task ◽  
Autonomous Driving ◽  
Transportation Systems ◽  
Text Detection ◽  
Text Recognition ◽  
Test Time ◽  
Average Precision ◽  
Detection And Recognition

Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.

scholarly journals Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu
Keyword(s):  
Cyber Security ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Assessment Method ◽  
Transportation Systems ◽  
Cyber Attacks ◽  
Large Set ◽  
Security Attacks ◽  
Physical Damage

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

A Low-Cost Real-World Planning Strategy for Deploying a Dedicated Short-Range Communications Roadside Unit on a Highway Off-Ramp

2018 ◽  
Vol 2672 (19) ◽  
pp. 124-134 ◽  
Author(s):  
Jonathan B. Walker ◽  
Kevin Heaslip
Keyword(s):  
Real World ◽  
Intelligent Transportation Systems ◽  
Short Range ◽  
Environmental Changes ◽  
Low Cost ◽  
Transportation Systems ◽  
Planning Strategy ◽  
Dedicated Short Range Communications ◽  
Roadside Unit ◽  
World Environment

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

scholarly journals Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Khashayarfard ◽  
Habibollah Nassiri
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Safety Assessment ◽  
Human Error ◽  
Transportation Systems ◽  
Text Messages ◽  
Assessment Model ◽  
Driving Simulators ◽  
Time To Collision ◽  
Unsignalized Intersections

Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.

scholarly journals OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 560 ◽  
Author(s):  
Amira Mimouna ◽  
Ihsen Alouani ◽  
Anouar Ben Khalifa ◽  
Yassin El Hillali ◽  
Abdelmalik Taleb-Ahmed ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Radar Data ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Future Research ◽  
Acoustic Data ◽  
Cluttered Environment ◽  
Environment Perception

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

Effect of route guidance and route scheduling systems on courier pickup and delivery operations: a simulation study

2001 ◽  
Vol 28 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Omar H Choudhry ◽  
Ata M Khan
Keyword(s):  
Simulation Study ◽  
Intelligent Transportation Systems ◽  
Route Planning ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Operating Conditions ◽  
Route Optimization ◽  
Pickup And Delivery ◽  
Route Guidance ◽  
Time Savings

The efficiency of pickup-and-delivery (P&D) activities of urban courier is adversely impacted by traffic congestion and lack of logistical innovations. The high frequency of P&D tasks offers the opportunity to achieve substantial savings in time through the implementation of route optimization, route guidance, and scheduling innovations. These innovations would require the intelligent transportation systems (ITS) technologies for their implementation. This paper describes how such time savings can be obtained. For testing the effect of network and operating conditions on time savings, a simulation study was undertaken on synthesized networks and logistical requirements. The results show that route guidance and scheduling systems yield highly significant time savings in the P&D operation of the courier industry.Key words: courier, intelligent transportation systems, route guidance, route planning, simulation.

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