Microscopic traffic simulation with reactive driving agents

Ehlert, Patrick; Rothkrantz, Léon J. M.

doi:10.1109/itsc.2001.948773

Cited by 77 publications

(43 citation statements)

References 2 publications

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“…A sophisticated version of the macroscopic simulation method can identify three or more groups of vehicles inside the traffic flow which share identical characteristics (typically size and speed) such as cars, motorbikes and buses. This allows for some degree of control over the simulated traffic as these three main groups are characterised by different driving speeds and movements (Ehlert & Rothkrantz, 2001). Yet, these groups do not have any differentiating attributes between individual vehicles in the same group; individual driving characteristics are ignored in favour of simulation of very large numbers of vehicles moving in large urban or motorway complexes.…”

Section: Macroscopic Simulation Approachmentioning

confidence: 99%

“…This in-detail approach could not be achieved solely by the macroscopic approach, as it does not address the impact to individual vehicles' problems on the traffic (accidents, vehicle break downs etc). Contrary it solely investigates the impact of such individual incidents on the whole traffic flow (Ehlert & Rothkrantz, 2001) rather the individual behaviour of neighbouring vehicles. Thus the individual behaviour of each agent catered for realistic interactions between the robots regardless of their group identity.…”

Section: Microscopic Simulation Approachmentioning

confidence: 99%

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Artificial Intelligence Rationale for Autonomous Vehicle Agents Behaviour in Driving Simulation Environment

Charissis¹,

Papanastasiou²

2008

Advances in Robotics, Automation and Control

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In recent times, in-vehicle notifications have proliferated with a focus on the exhibition of technological prowess rather than the fulfilment of actual driving needs. In effect, information portrayed by automotive infotainment devices, while useful, is often ignored by the driver due to field of view limitations associated with traditional instrumentation panels. Not surprisingly, under poor visibility conditions and at motorway-level driving speeds, such systems do not effectively present useful information to the user. Contemporary Head-Up Display (HUD) experiments have focused on adapting the aviation-specific characteristics of HUDs to driver-specific needs, obsolescing functionality and simplifying operations where necessary. The more mature approach of these preceding works has revealed that although in-vehicle HUD technological advances have overcome most implementation issues, the related user-centred interface design is in its infancy prohibiting the HUD's unique features from being successfully exploited. Towards addressing this issue, in previous work, we have designed and implemented a functional prototype of a Human Machine Interface (HMI). Specifically, the proposed HMI system introduces a novel design for an automotive HUD interface, which aims to improve the driver's spatial awareness and response times under low visibility conditions. Particular emphasis has been placed on the prioritisation and effective presentation of information available through vehicular sensors, which would assist, without distracting, the driver in successfully navigating the vehicle. In order to evaluate the effectiveness of the proposed HUD system, we developed a driving simulator based on an open source racing program. Leveraging an open source solution has resulted in manageable levels of incurred expenses whilst allowing us to deliver a flexible simulation application for HUD evaluation. This chapter discusses the artificial intelligence (AI) as developed for the agent vehicles of our open source driving simulator. The simulator was explicitly designed to measure driver's performance when using the proposed HUD interface and compares its effectiveness to traditional instrumentation techniques. Intuitively, human cognition complexity poses the largest challenge for creating a model of life-like driver's behaviour for any type of traffic flow. Presuming that specific driving characteristics apply to all human drivers, as dictated by common sense, an attempt was made to form a generic reaction Open Access Database www.i

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Section: Macroscopic Simulation Approachmentioning

confidence: 99%

Section: Microscopic Simulation Approachmentioning

confidence: 99%

Artificial Intelligence Rationale for Autonomous Vehicle Agents Behaviour in Driving Simulation Environment

Charissis¹,

Papanastasiou²

2008

Advances in Robotics, Automation and Control

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“…Their advantages are computational simplicity and fast simulation speeds. In microscopic models, each vehicle is represented individually and traffic conditions arise as a consequence of vehicle interactions, closer to real life [14].These models are based on the acceleration function, with inputs such as the distance to the vehicle in front, adjacent lane vehicles [12]or even psychological factors [15].Individual driver modeling allows for dynamic route choices, which also impact the overall traffic conditions. Mesoscopic models combine these characteristics, by including individual vehicles and routes.…”

Section: Introductionmentioning

confidence: 99%

A Massive Multilevel-parallel Microscopic Traffic Simulator with Gridlock Detection and Solving

Siromascenko¹,

Lungu²

2013

SIC

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Abstract:Traffic simulators based on microscopic models are a detailed approach to infrastructure and policy evaluation. They allow a closer to reality representation of the factors that influence traffic flow: individual driver and vehicle attributes, dynamic route decisions, lane changing and restrictions, driver cooperation. Such aspects add to the complexity and volume of computations, leading to slower simulation speeds compared to macroscopic models. Also, route restrictions can lead to gridlocks, a common problem in such simulations. In this paper, we propose a multi-level parallel architecture for the TrafficWeb microscopic traffic simulator. The solution combines random load allocation, for multithreaded processing, and distributed parallelization, through geographical domain decomposition. Adaptive load balancing is used for optimizing the distributed processing speed. Gridlock detection and solving are employed through efficient parallel and distributed algorithms, significantly decreasing their cost. Performance tests show an overall efficiency of 85% for the multilevel-parallel architecture, on a cluster with 5 nodes, each having 4 cores. This allows simulating metropolitan traffic 85 times faster than in real time.

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“…(Zhang et al, 2005) uses a MAS for simulating single-lane roads and provide agents with a decision tree that allows them to adapt their velocity and acceleration depending on environmental constraints and desired velocity. (Ehlert and Rothkrantz, 2001), defines profiles of agents and analyses the impact of of agent behavior to the system. The two profiles defined are: fast and aggressive or slow and careful.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Traffic Simulation Framework for Evaluating the Impact of Traffic Lights

Cajias¹,

González-Pardo²,

Camacho

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Abstract:The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.

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Microscopic traffic simulation with reactive driving agents

Cited by 77 publications

References 2 publications

Artificial Intelligence Rationale for Autonomous Vehicle Agents Behaviour in Driving Simulation Environment

Artificial Intelligence Rationale for Autonomous Vehicle Agents Behaviour in Driving Simulation Environment

A Massive Multilevel-parallel Microscopic Traffic Simulator with Gridlock Detection and Solving

A Multi-Agent Traffic Simulation Framework for Evaluating the Impact of Traffic Lights

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