Project

Distributed Vehicular computing and communications.

Goal: Employ emerging communication and computation technologies. Develop frameworks, edge-assisted architectures, and proof-of-concept prototypes to assist the soon-to-emerge vehicular applications and services. Vehicular applications and services include the prediction of potential accidents and congestions, unsafe behavior identification, and increase the efficiency of transportation.

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Project log

Ahmad Alhilal
added a research item
Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.
Pengyuan Zhou
added a research item
The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms.
Tristan Braud
added 3 research items
Vehicular communication applications require an efficient communication architecture for timely information delivery. Centralized, cloud-based infrastructures present latencies too high to satisfy the requirements of emergency information processing and transmission, while Vehicle-to-Vehicle communication is too variable for reliable in-time information transmission. In this paper, we present EAVVE, a novel Vehicle-to-Everything system, consisting of vehicles with and without comprehensive data processing capabilities, facilitated by edge servers co-located with roadside units. Adding computation capabilities at the edge of the network allows reducing the overall latency compared to vehicle-to-cloud and makes up for scenarios in which in-vehicle computational power is not sufficient to satisfy the service demand. To improve the offloading efficiency, we propose a decentralized algorithm for real-time task scheduling and a client/server algorithm for information filtering. We demonstrate the practical applications of EAVVE with a bandwidth-hungry, latency constrained real-life prototype system that connects vehicular vision through Augmented Reality vision. We evaluate this prototype system with real-life road tests. We complement this practical evaluation with extensive simulations based on real-world base station and vehicular traffic data to demonstrate the scalability of EAVVE and its performance in citywide scenarios. EAVVE decreases the latency by 42.6% and 78.7% compared to local and remote cloud solutions while relaxing congestion at the bottleneck by 99% with reasonable infrastructure expenditure.
The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms.
The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms.
Tristan Braud
added a research item
Vehicular communication applications require an efficient communication architecture for timely information delivery. Centralized, cloud-based infrastructures present latencies too high to satisfy the requirements of emergency information processing and transmission, while Vehicle-to-Vehicle communication is too variable for reliable in-time information transmission. In this paper, we present EAVVE, a novel Vehicle-to-Everything system, consisting of vehicles with and without comprehensive data processing capabilities, facilitated by edge servers co-located with roadside units. Adding computation capabilities at the edge of the network allows reducing the overall latency compared to vehicle-to-cloud and makes up for scenarios in which in-vehicle computational power is not sufficient to satisfy the service demand. To improve the offloading efficiency, we propose a decentralized algorithm for real-time task scheduling and a client/server algorithm for information filtering. We demonstrate the practical applications of EAVVE with a bandwidth-hungry, latency constrained real-life prototype system that connects vehicular vision through Augmented Reality vision. We evaluate this prototype system with real-life road tests. We complement this practical evaluation with extensive simulations based on real-world base station and vehicular traffic data to demonstrate the scalability of EAVVE and its performance in citywide scenarios. EAVVE decreases the latency by 42.6% and 78.7% compared to local and remote cloud solutions while relaxing congestion at the bottleneck by 99% with reasonable infrastructure expenditure.
Ahmad Alhilal
added a research item
Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Vehicles, roadside units, and other road users can collaborate to deliver novel services and applications. These services and applications require 1) massive volumes of heterogeneous and continuous data to perceive the environment, 2) reliable and low-latency communication networks, 3) real-time data processing that provides decision support under application-specific constraints. Addressing such constraints introduces significant challenges for current communication and computing technologies. Relatedly, the fifth generation of cellular networks (5G) was developed to respond to communication challenges by providing for low-latency, high-reliability, and high bandwidth communications. As a major part of 5G, edge computing allows data offloading and computation at the edge of the network, ensuring low-latency and context-awareness, and 5G efficiency. In this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data.
Ahmad Alhilal
added a research item
Traffic congestion is worsening in every major city and brings increasing costs to governments and drivers. Vehicular networks provide the ability to collect more data from vehicles and roadside units, and sense traffic in real time. They represent a promising solution to alleviate traffic jams in urban environments. However, while the collected information is valuable, an efficient solution for better and faster utilization to alleviate congestion has yet to be developed. Current solutions are either based on mathematical models, which do not account for complex traffic scenarios or small-scale machine learning algorithms. In this paper, we propose ERL, a solution based on Edge Computing nodes to collect traffic data. ERL alleviates congestion by providing intelligent optimized traffic light control in real time. Edge servers run fast reinforcement learning algorithms to tune the metrics of the traffic signal control algorithm ran for each intersection. ERL operates within the coverage area of the edge server, and uses aggregated data from neighboring edge servers to provide city-scale congestion control. The evaluation based on real map data shows that our system decreases 48.71% average waiting time and 32.77% trip duration in normally congested areas, with very fast training in ordinary servers.
Ahmad Alhilal
added a project goal
Employ emerging communication and computation technologies. Develop frameworks, edge-assisted architectures, and proof-of-concept prototypes to assist the soon-to-emerge vehicular applications and services. Vehicular applications and services include the prediction of potential accidents and congestions, unsafe behavior identification, and increase the efficiency of transportation.