1,721,051 research outputs found
Comparison between different energy management algorithms for an urban electric bus with hybrid energy storage system based on battery and supercapacitors
No full textElectric vehicles are an interesting research field for the automotive industry, especially for fully electrical urban buses. Their particular path-defined frequent and consecutive stops close together encourage the usage of supercapacitors, which have a longer service life than rechargeable batteries, and the battery would only be used as a backup energy source. This means a hybrid energy system where an energy management function splits the power request between the two onboard energy storage systems. Two different real-time control algorithms previously developed are briefly presented and numerically tested by means of virtual simulation in order to compare their different behaviour and evaluate their performance compared to an optimal offline control logic based on the dynamic programming approach
Comparison of Distributed Autonomous Vehicle Control Logics in presence of multiple UGV by means of numerical and experimental tests
No full textThis paper presents a comparison between different distributed control algorithms for autonomous vehicles. The logics presented allow multiple vehicles to perform a race circuit without any collision. The comparison of the logics is carried out introducing different performance indexes, e.g. lap time, overall trajectory length, computational time, etc. Also experimental tests are carried out on small prototype RC cars on a demo race circuit (a simplified version of Monza F1 race circuit). For these tests, the logic is running on an ODROID-XU4, a powerful low-cost Single Board computer mounted on each prototype vehicle
Green Light Optimal Speed Advisory Customization for Urban Buses: an Experimental Approach
No full textUrban transportation systems are undergoing significant transformations to mitigate environmental impacts while enhancing efficiency and passenger experience. In this context, optimizing bus operations allows to achieve these goals. This paper presents an experimental approach to customize Green Light Optimal Speed Advisory (GLOSA) systems specifically tailored for urban buses (B-GLOSA). B-GLOSA systems provide real-time speed recommendations to the bus driver, aiming to synchronize the vehicle movement with traffic signal timing, thereby reducing energy consumption while improving travel time reliability and passengers comfort. However, existing GLOSA implementations primarily focus on private vehicles and lack customization for the unique characteristics of urban buses that must stop at specific locations for a certain amount of time to allow passengers to load and unload. Our proposed approach involves a comprehensive experimental framework, integrating real-world data analysis, simulation modeling, and field tests. The experimental results demonstrate the potential of B-GLOSA systems in optimizing urban bus operations while reducing energy consumption by 26% and longitudinal acceleration RMS by 21%
RobustStateNet: Robust ego vehicle state estimation for Autonomous Driving
Full text linkControl of an ego vehicle for Autonomous Driving (AD) requires an accurate definition of its state. Implementation of various model-based Kalman Filtering (KF) techniques for state estimation is prevalent in the literature. These algorithms use measurements from IMU and input signals from steering and wheel encoders for motion prediction with physics-based models, and a Global Navigation Satellite System(GNSS) for global localization. Such methods are widely investigated and majorly focus on increasing the accuracy of the estimation. Ego motion prediction in these approaches does not model the sensor failure modes and assumes completely known dynamics with motion and measurement model noises. In this work, we propose a novel Recurrent Neural Network (RNN) based motion predictor that parallelly models the sensor measurement dynamics and selectively fuses the features to increase the robustness of prediction, in particular in scenarios where we witness sensor failures. This motion predictor is integrated into a KF-like framework, RobustStateNet that takes a global position from the GNSS sensor and updates the predicted state. We demonstrate that the proposed state estimation routine outperforms the Model-Based KF and KalmanNet architecture in terms of estimation accuracy and robustness. The proposed algorithms are validated in the modified NuScenes CAN bus dataset, designed to simulate various types of sensor failures
Enhanced Traffic Light Guidance for Safe and Energy-Efficient Driving: A Study on Multiple Traffic Light Advisor (MTLA) and 5G Integration
Full text linkThis paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm's implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain
A Sampling-Based Approach to Urban Motion Planning Games with Stochastic Dynamics
Full text linkUrban driving is a challenging task that requires autonomous agents to account for the stochastic dynamics and interactions with other vehicles. In this paper, we propose a novel framework that models urban driving as a stochastic generalized Nash equilibrium problem (SGNEP) and solves it using information-theoretic model predictive control (IT-MPC). By exploiting the cooperative nature of urban driving, we transform the SGNEP into a stochastic potential game (SPG), which has desirable convergence guarantees. Furthermore, we provide an algorithm for isolating interacting vehicles and thus factorizing a game into multiple sub-games. Finally, we solve for the open-loop generalized Nash equilibrium of a stochastic game utilizing a sampling-based technique. We solve the problem in a receding-horizon fashion, and apply our framework to various urban scenarios, such as intersections, lane merges, and ramp merges, and show that it can achieve safe and efficient multi-agent navigation
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