1,720,965 research outputs found
Coordinated traffic lights and auction intersection management in a mixed scenario
No full textIoT (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
Smart Parking for All: Equipped and Non-equipped Vehicles in Smart Cities
No full textThe 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
Improving urban viability through smart parking
No full textIn 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
High-Performance Feature Extraction for GPU -Accelerated ORB-SLAMx
No full textIn the autonomous vehicles field, localization is a crucial aspect. While the ORB-SLAM algorithm is a recognized solution for these tasks, it poses challenges due to its computational intensity. Although accelerated implementation exists, a bottleneck persists in the Point Filtering phase which relies on the Distribute Octree algorithm that is not suitable for GPU processing. In this paper, we introduce a novel GPU-suitable algorithm designed to enhance the Point Filtering step, surpassing Distribute Octree. We conducted a comprehensive comparison with state-of-the-art CPU and GPU implementations, considering both computational time and trajectory accuracy. Our experimental results, demonstrate significant speed-ups up to 3x compared to previous contributions
Optimized Local Path Planner Implementation for GPU-Accelerated Embedded Systems
Full text linkAutonomous 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
GPU implementation of the Frenet Path Planner for embedded autonomous systems: A case study in the F1tenth scenario
Full text linkAutonomous vehicles are increasingly utilized in safety-critical and time-sensitive settings like urban environments and competitive racing. Planning maneuvers ahead is pivotal in these scenarios, where the onboard compute platform determines the vehicle's future actions. This paper introduces an optimized implementation of the Frenet Path Planner, a renowned path planning algorithm, accelerated through GPU processing. Unlike existing methods, our approach expedites the entire algorithm, encompassing path generation and collision avoidance. We gauge the execution time of our implementation, showcasing significant enhancements over the CPU baseline (up to 22x of speedup). Furthermore, we assess the influence of different precision types (double, float, half) on trajectory accuracy, probing the balance between completion speed and computational precision. Moreover, we analyzed the impact on the execution time caused by the use of Nvidia Unified Memory and by the interference caused by other processes running on the same system. We also evaluate our implementation using the F1tenth simulator and in a real race scenario. The results position our implementation as a strong candidate for the new state-of-the-art implementation for the Frenet Path Planner algorithm
Brief Announcement: Optimized GPU-accelerated Feature Extraction for ORB-SLAM Systems
No full textReducing 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
Managing Human-driven and Autonomous Vehicles at Smart Intersections
No full textAuction-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
Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections
Full text linkWith 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
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