VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach

Maalej, Yassine; Sorour, Sameh; Abdel-Rahim, Ahmed; Guizani, Mohsen

doi:10.1109/glocom.2017.8254480

Cited by 20 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, [40] and [22] incorporated deep learning into the V2V system: multiple intelligent vehicles share the intermediate features output by the neural network to promote the vehicle's perception capability. As for the dataset, [5,24,42] simulated the V2V scenarios with different frames from KITTI [11]. Yet, they were unrealistic for not capturing the measurements at the same time.…”

Section: Related Workmentioning

confidence: 99%

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

Li¹,

Ma²,

An³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

Li¹,

Ma²,

An³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…XIV. POST ALIGNMENT AND FUSION Maalej et al presented in [160] a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne LIDAR scans, and V2V exchanged BSMs over DSRC. Based on the adapted Darknet's CNN, the pixel-wise adjacency coordinates of moments are derived from the bounding boxes of recognized objects of KITTI frames.…”

Section: Part 4: Towards Vcs Meeting Autonomous Vehiclesmentioning

confidence: 99%

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

Maalej¹,

Balti²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

For decades, researchers on Vehicular Ad-hoc Networks (VANETs) and autonomous vehicles presented various solutions for vehicular safety and autonomy, respectively. Yet, the developed work in these two areas has been mostly conducted in their own separate worlds, and barely affect one-another despite the obvious relationships. In the coming years, the Internet of Vehicles (IoV), encompassing sensing, communications, connectivity, processing, networking, and computation is expected to bridge many technologies to offer value-added information for the navigation of self-driving vehicles, to reduce vehicle on board computation, and to deliver desired functionalities. Potentials for bridging the gap between these two worlds and creating synergies of these two technologies have recently started to attract significant attention of many companies and government agencies. In this article, we first present a comprehensive survey and an overview of the emerging key challenges related to the two worlds of Vehicular Clouds (VCs) including communications, networking, traffic modelling, medium access, VC Computing (VCC), VC collation strategies, security issues, and autonomous driving (AD) including 3D environment learning approaches and AD enabling deep-learning, computer vision and Artificial Intelligence (AI) techniques. We then discuss the recent related work and potential trends on merging these two worlds in order to enrich vehicle cognition of its surroundings, and enable safer and more informed and coordinated AD systems. Compared to other survey papers, this work offers more detailed summaries of the most relevant VCs and ADs systems in the literature, along with some key challenges and insights on how different technologies fit together to deliver safety, autonomy and infotainment services.

show abstract

“…1. WAVE recommends that, for a fixed SI equal to 100ms, every vehicle needs to be tuned to the CCH in order to exchange their safety related messages with neighboring vehicles as detailed in [30] and in [31]. WAVE differs between various Quality of Service (QoS) levels by using specific fields in the packets that are sent over CCH or SCH.…”

Section: A Wave 80211p Macmentioning

confidence: 99%

CUDA-Accelerated Application Scheduling in Vehicular Clouds Under Advanced Multichannel Operations in WAVE

Maalej,

Balti

2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper presents a novel Advanced Activity-Aware (AAA) scheme to optimize and improve Multi-Channel Operations based on the IEEE 1609.4 standard in wireless access vehicular environments (WAVE). The proposed scheme relies on the awareness of the vehicular safety load to dynamically find an optimal setup for switching between service channel intervals (SCHI) and control channel intervals (CCHI). SCHI are chosen for non-critical applications (e.g. infotainment), while CCHI are utilized for critical applications (e.g. safety-related). We maximize the channel utilization without sacrificing other application requirements such as latency and bandwidth. Our scheme is implemented and evaluated network simulator-3 (NS3). We guarantee the default Synchronization Interval (SI), like implemented by the standard in vehicular ad hoc networks (VANETs), when tested on real-time simulations of vehicular cloud (VC) load and VANET setups using NS3. We also evaluate a Markov Decision Process (MDP) based scheme and a fast greedy heuristic to optimize the problem of vehicular task placement with both IEEE 1609.4 and an opportunistic V2I version of the proposed AAA scheme. Our solution offers the reward of the VC by taking into account the overall utilization of the distributed virtualized VCs resources and vehicular bag-oftasks (BOTs) placements both sequentially and in parallel. We present the simulation metrics proving that our proposed solution significantly improve the throughput and decreases the average delay of uploaded packets used for non-safety applications, while maintaining reliable communication (via CCHI) for safety-related applications similar to the IEEE 1609.4 standard.Index Terms-vehicular cloud (VC), CUDA, dedicated short range communication (DSRC), wireless access vehicular environments (WAVE), vehicle to infrastructure (V2I), vehicle to vehicle (V2V), Markov decision process (MDP), IEEE 1609.4, network simulator-3 (NS3), service channel (SCH), Control channel (CCH).

show abstract

VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach

Cited by 20 publications

References 12 publications

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

CUDA-Accelerated Application Scheduling in Vehicular Clouds Under Advanced Multichannel Operations in WAVE

Contact Info

Product

Resources

About