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FILIPPO MUZZINI

Dottorando
Dipartimento di Scienze Fisiche, Informatiche e Matematiche

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Pubblicazioni

2024 - Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario [Articolo su rivista]
Muzzini, Filippo; Montangero, Manuela
abstract

2024 - Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections [Articolo su rivista]
Cabri, G.; Lugli, M.; Montangero, M.; Muzzini, F.
abstract

With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario.

2023 - Brief Announcement: Optimized GPU-accelerated Feature Extraction for ORB-SLAM Systems [Relazione in Atti di Convegno]
Muzzini, F.; Capodieci, N.; Cavicchioli, R.; Rouxel, B.
abstract

Reducing the execution time of ORB-SLAM algorithm is a crucial aspect of autonomous vehicles since it is computationally intensive for embedded boards. We propose a parallel GPU-based implementation, able to run on embedded boards, of the Tracking part of the ORB-SLAM2/3 algorithm. Our implementation is not simply a GPU port of the tracking phase. Instead, we propose a novel method to accelerate image Pyramid construction on GPUs. Comparison against state-of-the-art CPU and GPU implementations, considering both computational time and trajectory errors shows improvement on execution time in well-known datasets, such as KITTI and EuRoC.

2023 - Coordinated traffic lights and auction intersection management in a mixed scenario [Relazione in Atti di Convegno]
Muzzini, F.; Capodieci, N.; Montangero, M.
abstract

IoT (Internet-of-Things) powered devices can be exploited to connect vehicles to a smart city infrastructure and thus allow vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. Such a complex system is aimed at improving our way of living in the city by mitigating the effect of traffic congestion, and consequently stress and pollution. We place ourselves in a transient scenario in which next generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT-capabilities. In this work we focus on intersection management and, in particular, on reusing existing traffic lights empowered by a new management systems. We propose an auction based system in which traffic lights are able to exchange contextual information with vehicles and the nearby traffic lights with the aim of reducing average waiting times at intersections and consequently, overall trip times. We evaluate our proposal using the well known MATSim transport simulator, by using a synthetic Manhattan map and a new map we build on an urban area located in our town, in Norther Italy. In such an area, instrumentation through IoT devices has been set up as part of an European research project. Results show that the proposal is better performing than the classical Fixed Time Control system currently adopted for traffic lights, and then auction strategies that do not exploit coordination among nearby traffic lights.

2023 - Improving urban viability through smart parking [Articolo su rivista]
Muzzini, F.; Capodieci, N.; Montangero, M.
abstract

In Smart Cities, vehicles can share intentions and retrieve information through IoT (Internet-of-Things) devices. This work proposes a reservation system that exploits communication between vehicles and city infrastructure to reduce the time a road user needs to find parking. The system is designed to manage the coexistence of next-generation vehicles, that communicate with city infrastructure and traditional vehicles that don't. We reconstructed an IoT-instrumented urban area using the MATSim simulator to evaluate the system. The proposed system reduces the parking search time of next-generation vehicles without disadvantaging traditional vehicles, making it a candidate for scenarios where both vehicle types coexist.

2023 - Optimized Local Path Planner Implementation for GPU-Accelerated Embedded Systems [Articolo su rivista]
Muzzini, F.; Capodieci, N.; Ramanzin, F.; Burgio, P.
abstract

Autonomous vehicles are latency-sensitive systems. The planning phase is a critical component of such systems, during which the in-vehicle compute platform is responsible for determining the future maneuvers that the vehicle will follow. In this paper, we present a GPU-accelerated optimized implementation of the Frenet Path Planner, a widely known path planning algorithm. Unlike the current state-of-the-art, our implementation accelerates the entire algorithm, including the path generation and collision avoidance phases. We measure the execution time of our implementation and demonstrate dramatic speedups compared to the CPU baseline implementation. Additionally, we evaluate the impact of different precision types (double, float, half) on trajectory errors to investigate the tradeoff between completion latencies and computation precision.

2023 - Smart Parking for All: Equipped and Non-equipped Vehicles in Smart Cities [Relazione in Atti di Convegno]
Muzzini, F.; Montangero, M.; Capodieci, N.
abstract

The current trend in designing cities is to think them as smart environments that are constantly connected with road users. For this purpose, a smart city is implemented as a collection of IoT (Internet-of-Things) powered devices set up in order to connect vehicles to their surrounding infrastructure. In this way, road users share their intentions while retrieving useful information from the smart city itself. This complex and distributed system must be then tailored to improve viability performance metrics such as reducing traffic congestion, optimizing accident response and everything else related to transportation in urban areas. In this work we focus on parking management in a scenario in which next generation vehicles will be able to communicate with the surrounding infrastructure and will coexist with traditional vehicles with limited or absent IoT-capabilities. We propose a reservation mechanism able to exploit communication at infrastructure level, with the goal of reducing the time needed to find a free parking spot close to destination. We evaluate our proposed mechanisms using the well known MATSim transport simulator.

2021 - About auction strategies for intersection management when human-driven and autonomous vehicles coexist [Articolo su rivista]
Cabri, Giacomo; Gherardini, Luca; Montangero, Manuela; Muzzini, Filippo
abstract

Autonomous vehicles are appearing in our streets, and will soon populate our transportation infrastructures, which must be equipped with appropriate sensors and actuators in order to manage vehicles in a fruitful way. Besides the infrastructures, appropriate algorithms must be defined in order to coordinate the vehicles and to enable them to exploit the resources in a fair yet effective way. In the immediate future, autonomous vehicles must coexist human-driven vehicles, and this transitory scenario poses several challenges in coordinating both kinds to exploit street resources. One of these resources, whose management is quite challenging, is represented by intersections: vehicles come and aim at passing the intersection, often as soon as possible, but they must compete with other vehicles having the same aim. A possible approach that has been used in literature to this problem uses auction based mechanisms. In this paper, we place ourselves in the above-mentioned transitory scenario in which both human-driven and autonomous vehicles will compete to cross intersections, and we investigate the effectiveness of auction-based mechanism to coordinate vehicles at intersections. We devise some simple auction policies, and assume vehicle coordination strategies that are suitable also for human drivers. Our results lead us to believe that, under these assumptions, simple auction mechanisms do not introduce advantages for what concern traveling times as they do in the case of exclusively autonomous vehicles.

2021 - Improving emergency response in the era of ADAS vehicles in the Smart City [Articolo su rivista]
Capodieci, N.; Cavicchioli, R.; Muzzini, F.; Montagna, L.
abstract

Management of emergency vehicles can be fostered within a Smart City, i.e. an urban environment in which many IoT devices are orchestrated by a distributed intelligence able to suggest to road users the best course of action in different traffic situations. By extending MATSim (Multi-Agent Transport Simulation Software), we design and test appropriate mitigation strategies when traffic accidents occur within an existing urban area augmented with V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure) capabilities and Advanced Driving Assisted cars (ADAS). Further, we propose traffic congestion models and related mechanisms for improving the necessary time for emergency vehicles to respond to accidents.

2020 - Exploiting Traffic Lights to Manage Auction-Based Crossings [Relazione in Atti di Convegno]
Muzzini, F.; Capodieci, N.; Montangero, M.
abstract

Auction-based crossing management approaches are used to design coordination policies for autonomous vehicles and improve smart intersections by providing differentiated latencies. In this paper, we propose and exploit an auction based mechanism for managing the urban traffic light infrastructure in which participant vehicles are either equipped or non-equipped. The difference between these two categories of vehicles is that only the equipped ones can actively participate to auctions through in-vehicle IoT-devices, i.e. they are able to communicate with the surrounding urban infrastructure. In this way, we aim to study the transitional period that will occur before the complete adoption of autonomous or strongly connected vehicles. Through extensive experiments and simulations, by comparing our mechanism to the traditional traffic light fixed-time-control approach, we studied the benefits and limitations, in term of waiting and trip times, when varying the subset of equipped vehicles and the available budget that can be used to participate to auctions.

2020 - Managing Human-driven and Autonomous Vehicles at Smart Intersections [Relazione in Atti di Convegno]
Cabri, G.; Montangero, M.; Muzzini, F.; Valente, P.
abstract

Auction-based crossing management approaches are used to design coordination policies for autonomous vehicles and improve smart intersections by providing for differentiated latencies. In this paper we exploit auction-based mechanisms to design a management intersections system re-using traffic lights and coordinating human driven and autonomous vehicles. We first describe in detail this system that uses already present traffic lights and the bidding policy of our auction mechanisms. We then describe our experimental scenario and the research issue that will be addressed by means of future simulations.