Cooperative Lane-Change and Longitudinal Behaviour Model Extension for TraffSim

Backfrieder, Christian; Ostermayer, Gerald; Lindorfer, Manuel; Mecklenbräuker, Christoph F.

doi:10.1007/978-3-319-39595-1_6

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Ref. [21] introduces a cooperative lane-changing model for scenarios with multiple congested lanes at intersections. Some following vehicles on the target lane decelerate to create enough space for coordinated maneuvers.…”

Section: Behavior Selection In Multi-vehicle Drivingmentioning

confidence: 99%

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Rong,

Meng,

Guo

et al. 2024

Sustainability

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Autonomous vehicles hold the potential to significantly improve traffic efficiency and advance the development of intelligent transportation systems. With the progression of autonomous driving technology, collaborative planning among multiple vehicles in autonomous driving scenarios has emerged as a pivotal challenge in realizing intelligent transportation systems. Serving as the cornerstone of unmanned mission decision-making, collaborative motion planning algorithms have garnered increasing attention in both theoretical exploration and practical application. These methods often follow a similar paradigm: the system initially discerns the driving intentions of each vehicle, subsequently assesses the surrounding environment, engages in path-planning, and formulates specific behavioral decisions. The paper discusses trajectory prediction, game theory, following behavior, and lane merging issues within the paradigm mentioned above. After briefly introducing the background of multi-vehicle autonomous driving, it provides a detailed description of the technological prerequisites for implementing these techniques. It reviews the main algorithms in motion planning, their functionalities, and applications in road environments, as well as current and future challenges and unresolved issues.

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Section: Behavior Selection In Multi-vehicle Drivingmentioning

confidence: 99%

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Rong,

Meng,

Guo

et al. 2024

Sustainability

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“…each unique combination of control settings and traffic demand, we simulate one hour of traffic in order to obtain meaningful results. The car-following behavior of all simulated vehicles is modeled by the Intelligent Driver Model (IDM) [23], lateral movements are simulated using the lane-change model MOBIL [24] and its Cooperative Longitudinal and Lane-Change Behavior Extension (CLLxT) [25]. For further information on the applied models and their recommended parameter ranges it is referred to the literature cited.…”

Section: A Simulation Setupmentioning

confidence: 99%

Modelling and Simulation of Isolated Traffic Control Strategies in TraffSim

Lindorfer

Backfrieder

Ostermayer

2018

International Journal of Simulation: Systems, Science &Amp; Technology

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Traffic signal controls have become an integral part of urban traffic management and operations and play a crucial role in both mitigating congestion and reducing or eliminating conflicts at intersections. Starting with the installation of the first traffic signal in London in the nineteenth century, signal control technology has evolved steadily. Nowadays, traffic signal control is not only used to provide the right-of-way at intersections, but also to improve traffic efficiency by means of minimizing waiting times and optimizing traffic flow. In this paper we present recent efforts to introduce different types of both time-based and actuated traffic signal control strategies into the microscopic traffic simulator TraffSim. Furthermore, we demonstrate the applicability of selected control strategies by simulating both an isolated four-way intersection and a multi-intersection scenario under consideration of varying traffic densities. Moreover, we quantify the improvements in traffic efficiency imparted by these control strategies in terms of intersection throughput, waiting times, and network load.

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“…Lombard et al [15] designed a cooperative lane-change policy to enable better road sharing among autonomous vehicles in which a specific protocol to determine the intersection point coordinates and acceptance criterion was defined. Backfrieder et al [16] proposed a cooperative lane-change and longitudinal behavior model for resolving situations with multiple congested lanes at an intersection. Some of the following vehicles in the target lane decelerated to make enough space to achieve the cooperative maneuver.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Lane-Change Maneuver for Multiple Automated Vehicles on a Highway

Luo

Yang

et al. 2019

Automot. Innov.

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With the development of vehicle-to-vehicle (V2V) communication, it is possible to share information among multiple vehicles. However, the existing research on automated lane changes concentrates only on the single-vehicle lane change with self-detective information. Cooperative lane changes are still a new area with more complicated scenarios and can improve safety and lane-change efficiency. Therefore, a multi-vehicle cooperative automated lane-change maneuver based on V2V communication for scenarios of eight vehicles on three lanes was proposed. In these scenarios, same-direction and intersectant-direction cooperative lane changes were defined. The vehicle that made the cooperative decision obtained the information of surrounding vehicles that were used to cooperatively plan the trajectories, which was called cooperative trajectory planning. The cooperative safety spacing model was proposed to guarantee and improve the safety of all vehicles, and it essentially developed constraints for the trajectory-planning task. Trajectory planning was treated as an optimization problem with the objective of maximizing safety, comfort, and lane-change efficiency under the constraints of vehicle dynamics and the aforementioned safety spacing model. Trajectory tracking based on a model predictive control method was designed to minimize tracking errors and control increments. Finally, to verify the validity of the proposed maneuver, an integrated simulation platform combining MATLAB/Simulink with CarSim was established. Moreover, a hardware-inthe-loop test bench was performed for further verification. The results indicated that the proposed multi-vehicle cooperative automated lane-change maneuver can achieve lane changes of multiple vehicles and increase lane-change efficiency while guaranteeing safety and comfort.

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Cooperative Lane-Change and Longitudinal Behaviour Model Extension for TraffSim

Cited by 8 publications

References 18 publications

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Modelling and Simulation of Isolated Traffic Control Strategies in TraffSim

Cooperative Lane-Change Maneuver for Multiple Automated Vehicles on a Highway

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