3D point cloud map based vehicle localization using stereo camera

Xu, Yuquan; John, Vijay; Mita, Seiichi; Tehrani, Hossein; Ishimaru, Kazuhisa; Nishino, Seiji

doi:10.1109/ivs.2017.7995765

Cited by 58 publications

(23 citation statements)

References 21 publications

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“…Recently, with the emerging of technologies such as cloud computing, Internet of Things and Artificial Intelligence, the Internet of Vehicles (IoV) has attracted numerous attentions for its significant advantages in reducing traffic accidents [29], mitigating traffic congestion [4], and providing various real-time convenience services. In the case of intelligent navigation [30], traffic scenes are transmitted to the on-board computer in real time, and the onboard computer will automatically plan the most convenient route for driver in which the congested route paths with the shortest distance will be abandoned. However, most of these applications require intensive computation and tight delay constraints [8], especially for applications which need to dynamically process video data and interact in real-time.…”

Section: Related Work a Computation And Communication In Vehiculmentioning

confidence: 99%

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Zhou

Xiong³

et al. 2019

IEEE Access

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As autonomous and connected vehicles are becoming a reality, mobile-edge computing (MEC) off-loading provides a promising paradigm to trade off between the long latency of clouding computing and the high cost of upgrading the on-board computers of vehicles. However, due to the randomness of task arrivals, vehicles always have a tendency to choose MEC server for offloading in a selfish way, which is not satisfactory for the social good of the whole system and even results in a failure possibility of some tasks due to the overflow of MEC servers. This paper elaborates the modeling of task arrival process and the influence of various offloading modes on computation cost. Interestingly, by formulating task arrivals as a compound process of vehicle arrivals and task generations, we found that the task arrival model for MEC servers does not belong to the standard Poisson distribution, which contradicts the popular assumption in most existing studies. Considering the load distribution and the prediction of cost, we propose a load-aware MEC offloading method, in which each vehicle makes MEC server selection based on the predicted cost with the updated knowledge on load distribution of MEC servers. Analysis and simulation show that the proposed scheme can achieve up to 65% reduction of total cost with almost 100% task success ratio. INDEX TERMS Mobile-edge computing, task arrival model, load-aware offloading, vehicular networks, load balance. I. INTRODUCTION With the development of the Internet of Vehicles (IoV), autonomous and connected vehicles are envisioned to provide a safer, greener [1], [2] and much more convenient [3], [4] transportation system for the public. Internet of vehicles are based on a core technology called Vehicular Ad Hoc Networks (VANETs) [5], [6], which is able to integrate the capabilities of wireless networks to vehicles. Vehicles may communicate with other vehicles directly in a vehicle-tovehicle fashion (V2V) [7], or communicate through road side units(RSUs), so called Vehicle-to-Infrastructure (V2I) communications [8]. IEEE 802 committee has defined wireless communication standard IEEE 802.11p [9] for V2I The associate editor coordinating the review of this manuscript and approving it for publication was Xu Chen. communication and the Federal Communications Commission allocated 75 MHz of bandwidth, which operates on 5.9 GHz channel for short range communications [10]. Due to the resource-limited on-board computers of vehicles, many potential applications, such as assistant accident avoidance [11]-[13], mobile crowd sensing [14] and augmented reality [15], which require significant computing power to process data generated by the vehicle sensors in a real-time fashion [16], pose a grand challenge to vehicular terminals. To offload the computation burden of vehicles, cloud computing [17], [18] has been adopted by gathering the computation tasks to central cloud servers, and sending back the results to local users. However, the long distance between cloud center and vehicles [19] results in long l...

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Section: Related Work a Computation And Communication In Vehiculmentioning

confidence: 99%

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Zhou

Xiong³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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“…As shown by the above example of the DARPA winner, autonomous driving common practices include separately addressing two stages of the process, one being perception and optional world model maintenance, and the other being the decision making process. The perception stage includes localization, which is often addressed using either Lidar (Levinson and Thrun, 2010), a camera (Brubaker et al, 2016), or a combination of both (Xu et al, 2017). Perception may also include offline obstacle mapping such as in SLAM (Valls et al, 2018), which will be discussed further on.…”

Section: Related Workmentioning

confidence: 99%

Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations

Zadok

Hirshberg

Biran

et al. 2019

Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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This paper describes the exploration and learnings during the process of developing a self-driving algorithm in simulation, followed by deployment on a real car. We specifically concentrate on the Formula Student Driverless competition. In such competitions, a formula race car, designed and built by students, is challenged to drive through previously unseen tracks that are marked by traffic cones. We explore and highlight the challenges associated with training a deep neural network that uses a single camera as input for inferring car steering angles in real-time. The paper explores in-depth creation of simulation, usage of simulations to train and validate the software stack and then finally the engineering challenges associated with the deployment of the system in real-world.

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“…El Zoghby et al introduced their distributed approach to dynamic maps whereby vehicles cooperate within a VANet (Vehicular Ad-hoc Network) in order to form a complete view of LDM [16]. Xu et al proposed an implementation of 3D point cloud map based on data collected from LiDAR units on vehicles [17]. This concept is the same as the high-definition 3D maps that were created within the project launched in 2016 under the support of the Japanese government program, Strategic Innovation Promotion Program Innovation of Automated Driving for Universal Services (SIP-adus).…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Map-based Framework for Real-Time Mapping of Vehicles and their Surroundings

Maiouak

Taleb

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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The great attraction that unmanned vehicles have gained in the past years has marked this century as the era of automation. In particular, automated vehicles have recently become the center of attention of many research activities. All of which have had one goal in common: achieving a complete or partial automation of driving functions, all while ensuring safety and security of other traffic agents. It is with this purpose in mind that the concept of dynamic maps has been introduced to allow vehicles to be aware of their surroundings and have precise knowledge of their environment and all of its components. Aiming for a mapping system that sets out a layered view of a vehicle vicinity ranging from highly-static to highly-dynamic data layers, researchers have taken different approaches. In this paper, we present a system, inspired from the concept of dynamic maps, that collects data from user vehicles and maps them out by location and time, keeping a history of all recorded information. We then evaluate the performance of the system module in charge of localization and tracking of users' devices, and present and discuss the obtained results.

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3D point cloud map based vehicle localization using stereo camera

Cited by 58 publications

References 21 publications

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations

A Dynamic Map-based Framework for Real-Time Mapping of Vehicles and their Surroundings

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