Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving

Recently, large amounts of data traffic from various sensors and image and navigation systems within vehicles are generated for autonomous driving. Broadband communication networks within vehicles have become necessary. New autonomous Ethernet networks are being considered as alternatives. The Ethernet-based in-vehicle network has been standardized in the IEEE 802.1 time-sensitive network (TSN) group since 2006. The Ethernet TSN will be revised and integrated into a subsequent version of IEEE 802.1Q-2018 published in 2018 when various new TSN-related standards are being newly revised and published. A TSN integrated environment simulator is developed in this paper to implement the main functions of the TSN standards that are being developed. This effort would minimize the performance gaps that can occur when the functions of these standards operate in an integrated environment. As part of this purpose, we analyzed the simulator to verify that the traffic for autonomous driving satisfies the TSN transmission requirements in the in-vehicle network (IVN) and the preemption (which is one of the main TSN functions) and reduces the overall End-to-End delay. An optimal guard band size for the preemption was also found for autonomous vehicles in our work. Finally, an IVN model for autonomous vehicles was designed and the performance test was conducted by configuring the traffic to be used for various sensors and electronic control units (ECUs).

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High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

Sensors ◽

10.3390/s21041131 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1131

Author(s):

Eduardo Sánchez Morales ◽

Julian Dauth ◽

Bertold Huber ◽

Andrés García Higuera ◽

Michael Botsch

Keyword(s):

State Estimation ◽

Autonomous Vehicles ◽

Current Trend ◽

Research Work ◽

Autonomous Driving ◽

Indoor Navigation ◽

Global Navigation Satellite Systems ◽

Navigation Systems ◽

Automated Driving ◽

Satellite Systems

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

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An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone

Sensors ◽

10.3390/s21010098 ◽

2020 ◽

Vol 21 (1) ◽

pp. 98

Author(s):

Dae-Young Kim ◽

Minwoo Jung ◽

Seokhoon Kim

Keyword(s):

Computer Simulations ◽

Autonomous Driving ◽

Vehicular Network ◽

Transmission Efficiency ◽

Transmission Delay ◽

Data Traffic ◽

Internet Of Vehicles ◽

High Bandwidth ◽

Average Transmission ◽

Vehicle Network

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.

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Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

Sensors ◽

10.3390/s20195450 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5450

Author(s):

Sorin Grigorescu ◽

Tiberiu Cocias ◽

Bogdan Trasnea ◽

Andrea Margheri ◽

Federico Lombardi ◽

...

Keyword(s):

Autonomous Vehicles ◽

Autonomous Driving ◽

Novel Technologies ◽

Learning Modules ◽

Network Bandwidth ◽

Inference Engines ◽

Control Units ◽

Probable Path ◽

Self Driving Cars ◽

Most Probable Path

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

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5G Mobile Services and Scenarios: Challenges and Solutions

Sustainability ◽

10.3390/su10103626 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3626 ◽

Cited By ~ 18

Author(s):

Yousaf Zikria ◽

Sung Kim ◽

Muhammad Afzal ◽

Haoxiang Wang ◽

Mubashir Rehmani

Keyword(s):

Mobile Network ◽

Future Internet ◽

Autonomous Driving ◽

Mobile Services ◽

Data Traffic ◽

Fifth Generation ◽

Wide Range ◽

Industrial Iot ◽

5G Network ◽

Massive Number

The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.

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Machine Learning for Communications

Entropy ◽

10.3390/e23070831 ◽

2021 ◽

Vol 23 (7) ◽

pp. 831

Author(s):

Vaneet Aggarwal

Keyword(s):

Machine Learning ◽

Augmented Reality ◽

Video Streaming ◽

Communication Networks ◽

Data Analytics ◽

Data Traffic ◽

Traffic Demand ◽

Performance Requirements

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

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Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641248 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1033-1037

Author(s):

Jiayuan Dong ◽

Emily Lawson ◽

Jack Olsen ◽

Myounghoon Jeon

Keyword(s):

Autonomous Vehicles ◽

Autonomous Driving ◽

Preference Ranking ◽

Uncanny Valley ◽

Tracking Data ◽

Effective Solution ◽

User Perception ◽

Future Studies ◽

Underlying Mechanisms ◽

The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

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Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽

10.3390/s21113783 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3783

Author(s):

Sumbal Malik ◽

Manzoor Ahmed Khan ◽

Hesham El-Sayed

Keyword(s):

Autonomous Vehicles ◽

Communication Technologies ◽

Autonomous Driving ◽

Use Cases ◽

Cooperative Driving ◽

Vehicle To Vehicle ◽

Future Challenges ◽

Vehicle To Infrastructure ◽

High Level ◽

Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641328 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1372-1376

Author(s):

Gaojian Huang ◽

Christine Petersen ◽

Brandon J. Pitts

Keyword(s):

Systematic Review ◽

Autonomous Vehicles ◽

Response Times ◽

Autonomous Driving ◽

Mental States ◽

Manual Control ◽

Monitoring Systems ◽

Wide Range ◽

Different Types ◽

The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

Journal of Imaging ◽

10.3390/jimaging7040061 ◽

2021 ◽

Vol 7 (4) ◽

pp. 61

Author(s):

David Urban ◽

Alice Caplier

Keyword(s):

Neural Network ◽

Autonomous Vehicles ◽

Depth Estimation ◽

Video Camera ◽

Obstacle Detection ◽

Navigation Systems ◽

Time To Collision ◽

Static Data ◽

Monocular Depth ◽

Fully Connected

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

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A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

Applied Sciences ◽

10.3390/app11136016 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6016

Author(s):

Jinsoo Kim ◽

Jeongho Cho

Keyword(s):

Object Detection ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

Detection Algorithm ◽

Detection Accuracy ◽

Cloud Data ◽

Detection Techniques ◽

Bounding Boxes ◽

Partially Occluded ◽

Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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