14 research outputs found
Stochastic stability for a model representing the intake manifold pressure of an automotive engine
The paper presents conditions to assure stochastic stability for a nonlinear model. The proposed model is used to represent the input-output dynamics of the angle of aperture of the throttle valve (input) and the manifold absolute pressure (output) in an automotive spark-ignition engine. The automotive model is second moment stable, as stated by the theoretical result—data collected from real-time experiments supports this finding.Peer ReviewedPostprint (author's final draft
Stochastic Stability For A Model Representing The Intake Manifold Pressure Of An Automotive Engine
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento CientÃfico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior (CAPES)The paper presents conditions to assure stochastic stability for a nonlinear model. The proposed model is used to represent the input-output dynamics of the angle of aperture of the throttle valve (input) and the manifold absolute pressure (output) in an automotive spark-ignition engine. The automotive model is second moment stable, as stated by the theoretical result-data collected from real-time experiments supports this finding.31Spanish Ministry of Economy and Competitiveness [DPI2015-64170-R/MINECO/FEDER, DPI2011-25822]Government of Catalonia (Spain) [2014SGR859]FAPESP [03/06736-7]CNPq [304856/2007-0]CAPES Grant Programa PVE [88881.030423/2013-01]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento CientÃfico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior (CAPES
Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep Reinforcement Learning
With the development of communication technologies, connected autonomous
vehicles (CAVs) can share information with each other. We propose a novel
behavior planning method for CAVs to decide actions such as whether to change
lane or keep lane based on the observation and shared information from
neighbors, and to make sure that there exist corresponding control maneuvers
such as acceleration and steering angle to guarantee the safety of each
individual autonomous vehicle. We formulate this problem as a hybrid partially
observable Markov decision process (HPOMDP) to consider objectives such as
improving traffic flow efficiency and driving comfort and safety requirements.
The discrete state transition is determined by the proposed feedback deep
Q-learning algorithm using the feedback action from an underlying controller
based on control barrier functions. The feedback deep Q-learning algorithm we
design aims to solve the critical challenge of reinforcement learning (RL) in a
physical system: guaranteeing the safety of the system while the RL is
exploring the action space to increase the reward. We prove that our method
renders a forward invariant safe set for the continuous state physical dynamic
model of the system while the RL agent is learning. In experiments, our
behavior planning method can increase traffic flow and driving comfort compared
with the intelligent driving model (IDM). We also validate that our method
maintains safety during the learning process.Comment: conferenc
Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming
Platooning connected and autonomous vehicles (CAVs) can improve traffic and
fuel efficiency. However, scalable platooning operations require junction-level
coordination, which has not been well studied. In this paper, we study the
coordination of vehicle platooning at highway junctions. We consider a setting
where CAVs randomly arrive at a highway junction according to a general renewal
process. When a CAV approaches the junction, a system operator determines
whether the CAV will merge into the platoon ahead according to the positions
and speeds of the CAV and the platoon. We formulate a Markov decision process
to minimize the discounted cumulative travel cost, i.e. fuel consumption plus
travel delay, over an infinite time horizon. We show that the optimal policy is
threshold-based: the CAV will merge with the platoon if and only if the
difference between the CAV's and the platoon's predicted times of arrival at
the junction is less than a constant threshold. We also propose two
ready-to-implement algorithms to derive the optimal policy. Comparison with the
classical value iteration algorithm implies that our approach explicitly
incorporating the characteristics of the optimal policy is significantly more
efficient in terms of computation. Importantly, we show that the optimal policy
under Poisson arrivals can be obtained by solving a system of integral
equations. We also validate our results in simulation with Real-time Strategy
(RTS) using real traffic data. The simulation results indicate that the
proposed method yields better performance compared with the conventional
method
The Role of Intelligent Transportation Systems and Artificial Intelligence in Energy Efficiency and Emission Reduction
Despite the technological advancements in the transportation sector, the
industry continues to grapple with increasing energy consumption and vehicular
emissions, which intensify environmental degradation and climate change. The
inefficient management of traffic flow, the underutilization of transport
network interconnectivity, and the limited implementation of artificial
intelligence (AI)-driven predictive models pose significant challenges to
achieving energy efficiency and emission reduction. Thus, there is a timely and
critical need for an integrated, sophisticated approach that leverages
intelligent transportation systems (ITSs) and AI for energy conservation and
emission reduction. In this paper, we explore the role of ITSs and AI in future
enhanced energy and emission reduction (EER). More specifically, we discuss the
impact of sensors at different levels of ITS on improving EER. We also
investigate the potential networking connections in ITSs and provide an
illustration of how they improve EER. Finally, we discuss potential AI services
for improved EER in the future. The findings discussed in this paper will
contribute to the ongoing discussion about the vital role of ITSs and AI
applications in addressing the challenges associated with achieving energy
savings and emission reductions in the transportation sector. Additionally, it
will provide insights for policymakers and industry professionals to enable
them to develop policies and implementation plans for the integration of ITSs
and AI technologies in the transportation sector.Comment: 25 pages, 4 figure
Planning of Truck Platoons: a Literature Review and Directions for Future Research
A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research
The operational and safety effects of heavy duty vehicles platooning
Abstract
Although researchers have studied the effects of platooning, most of the work done so far has focused on fuel consumption. There are a few studies that have targeted the impact of platooning on the highway operations and safety. This thesis focuses on the impact of heavy-duty vehicles (HDVs) platooning on highway characteristics. Specifically, this study aims at evaluating the effects of platooning of HDVs on capacity, safety, and CO2 emissions.
This study is based on a hypothetical model that was created using the VISSIM software. VISSIM is a powerful simulation software designed to mimic the field traffic flow conditions. For model validity, the model outputs were compared with recommended values from guidelines such as the Highway Capacity Manual (HCM) (Transportation Research Board, 2016).
VISSIM was used to obtain the simulation results regarding capacity. However, in addition to VISSIM, two other software packages were used to obtain outputs that cannot be assessed in VISSIM. MOVES and SSAM are two simulation software packages that were used for emission and safety metrics, respectively. Both software packages depended on input from VISSIM for analysis.
It was found that with the presence of HDVs in the model, the capacity, the emission of CO2, and the safety of the roadway would improve positively. A capacity of 4200 PCE/h/ln could be achieved when there are enough HDVs in platoons. Furthermore, more than 3% of the traffic flow emission of CO2 reduction is possible when 100% of the HDVs used in the model are in platoons. In addition to that, a reduction of more than 75% of the total number of conflicts might be obtained.
Furthermore, with the analysis of the full factorial method and the Design of Experiment (DOE) conducted by using Excel and Minitab respectively, it was possible to investigate the impact of the platoons’ factors on the highway parameters. Most of these factors affect the parameters significantly. However, the change in the desired speed was found to insignificantly affect the highway parameters, due to the high penetration rate.
Keywords: VISSIM, MOVES, SSAM, COM-interface, HDVs, Platooning, Number of Conflict