SUMO Based Platform for Cooperative Intelligent Automotive Agents

Alekszejenkó, Levente; Dobrowiecki, Tadeusz

doi:10.29007/sc13

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Given the stable driving in the tested traffic scenarios, an evaluation of the behavior in more complex scenarios, possibly with several conflict agents, would be interesting. Regarding scenario generation, a coupling with Alekszejenkó and Dobrowiecki (2019) seems possible. To make use of the inter-vehicle and network communication, the agent model could be tested in scenarios with several autonomous agents communicating either with each or other with nearby intersections.…”

Section: Discussionmentioning

confidence: 99%

Modelling autonomous vehicle interactions with bicycles in traffic simulation

Rampf

Grigoropoulos

Malcolm

et al. 2023

Front. Future Transp.

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With the undeniable advance of automated vehicles and their gradual integration in day-today urban traffic, many new technologies have been developed that offer great potential for this emerging field of research. However, testing automated vehicle technologies in real road traffic with vulnerable road users (VRUs) is still a complicated and time consuming procedure. The virtual development and evaluation of automated vehicles using simulation tools offers a good opportunity to test new functions efficiently. However, existing models prove to be insufficient in modeling the interaction between autonomous vehicles and vulnerable road users such as bicyclists and pedestrians. In this paper, an automated vehicle model for microscopic traffic simulation tools is developed with the open-source traffic simulation software Simulation of Urban Mobility (SUMO) and evaluated in common traffic interaction scenarios with bicyclists. A controller model is proposed using different path-finding algorithms from the field of robotic and automated vehicle research, which covers all important control levels of a self-driving vehicle. Finally, its performance is compared to existing car-following and lane-changing models. Results showcase that the autonomous vehicle model achieves either comparable results or has a much steadier and more realistic driving behavior when compared to existing driving models while interacting with bicyclists. The whole source code developed over the course of this work is freely accessible at: https://github.com/FlixFix/Kimarite.

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Section: Discussionmentioning

confidence: 99%

Modelling autonomous vehicle interactions with bicycles in traffic simulation

Rampf

Grigoropoulos

Malcolm

et al. 2023

Front. Future Transp.

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show abstract

“…7) Ballistic [38]: It is a physics-based prediction model that primarily predicts future positions based on the vehicle's current speed and acceleration. 8) SL2015 [39]: This is a model based on safety logic that considers various traffic situations and driver behaviors.…”

Section: Methods Studiedmentioning

confidence: 99%

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Liao,

Xiao,

Xing

et al. 2024

CMES

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Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework, enhancing the vehicle's capability to comprehend complex roundabout environments and make decisions. Furthermore, the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios, effectively mitigating traffic bottlenecks and augmenting throughput efficiency. Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO, and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process, the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles (AVs). Generally, the proposed PE-DDPG model could be employed for autonomous driving in complex scenarios with imperfect data.

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“…RR is a conventional pre-configured IM strategy that rotates greenyellow phases in a circular order for a fixed allocated time. The RR IM strategy serves vehicles from one roadway at a time in a fixed direction [28]. The RR IM approach is also called the uniform TLC due to the uniformly distributed green and yellow phases.…”

Section: Comparing Intersection Management Protocolsmentioning

confidence: 99%

Worst-Case Response Time of Mixed Vehicles at Complex Intersections

Reddy,

Almeida,

Kurunathan

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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Operating autonomous vehicles (AVs) and human-driven vehicles (HVs) at urban intersections while observing requirements of safety and service level is complex due not only to the existence of multiple inflow and outflow lanes, conflicting crossing zones, and low-speed conditions but also due to differences between control mechanisms of HVs and AVs. Intelligent intersection management (IIM) strategies can tackle the coordination of mixed AV/HV intersections while improving intersection throughput and reducing travel delays and fuel wastage in the average case. An endeavor relevant to traffic planning and safety is assessing whether given worst-case service levels can be met. Given a specific arrival pattern, this can be done via the worst-case response time (WCRT) that any vehicle experiences when crossing intersections. In this research line, this paper estimates WCRT upper bounds and discusses the analytical characterization of arrival and service curves, including estimating maximum queue length and associated worst-case waiting time for various traffic arrival patterns. This analysis is then used to compare six state-of-the-art intersection management approaches from conventional to intelligent and synchronous. The analytical results show the advantage of employing a synchronous management approach and are validated with the vehicles floating car data (timestamped location and speed) and simulations carried out using SUMO.INDEX TERMS Intelligent intersection management, intelligent transportation systems, mixed traffic, traffic waiting time, urban traffic management.

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SUMO Based Platform for Cooperative Intelligent Automotive Agents

Cited by 6 publications

References 11 publications

Modelling autonomous vehicle interactions with bicycles in traffic simulation

Modelling autonomous vehicle interactions with bicycles in traffic simulation

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Worst-Case Response Time of Mixed Vehicles at Complex Intersections

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