General Lane-Changing Model MOBIL for Car-Following Models

By Arne Kesting, Martin Treiber, Dirk Helbing. 2007

1,402 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/4d3e5e3ffa72ea858e44e05ef01fea45f6c04085

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In Automotive Engineering

In Control and Systems Engineering

In Transportation

Citations in the corpus

Edge Computing for Autonomous Driving: Opportunities and Challenges (2019, background): “[92] A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model MOBIL for car-following models,” Transp.”
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, methodology): “…use MOBIL, which is a general model (minimizing overall braking induced by lane change) to derive lane-changing rules for discretionary and mandatory lane changes for a broad class of car-following models [27]; the Intelligent Driving Model (IDM), a continuous microscopic single-lane model [28].”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, methodology): “In that work, the proposed method is compared with a baseline called IDM/MOBIL agent for expert driver behaviours [153], [154].”
Parting with Misconceptions about Learning-based Vehicle Motion Planning (2023, background): “There exist extensions of IDM [29] which focus on enabling lane changes on highways.”
A kinematic wave theory of lane-changing traffic flow (2005, background): “, 1961); similarly, lane-choice behaviors on why, when, where, and how a vehicle changes its lane are modeled by lane-changing models (Gipps, 1986; Yang, 1997; Toledo et al., 2003; Kesting et al., 2007).”
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning (2021, methodology): “For vehicles, we currently implement a rule-based policy that mixes the cruising, lane changing, emergency stopping behaviors with various driving models such as IDM and mobile policy [19].”
Adaptive cruise control design for active congestion avoidance (2008, methodology): “Particularly, the microscopic modeling approach allows for a natural representation of heterogeneous traffic consisting of ACC vehicles and manually driven vehicles (Kesting et al., 2007b; Davis, 2004; VanderWerf et al., 2002; Treiber and Helbing, 2001; Marsden et al., 2001; Minderhoud, 1999).”
Dense reinforcement learning for safety validation of autonomous vehicles (2023, methodology): “Specifically, we modified the Intelligent Driving Model (IDM) 50 and the Minimizing Overall Braking Induced by Lane change (MOBIL) model 51 as stochastic models to construct the simplified NDE models.”
Traffic jams: dynamics and control (2010, background): “One might also extend car-following models to make them capable of modelling multi-lane traffic or junctions, in which case lane changes need to be handled (Davis 2004; Kesting et al. 2007).”
Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach (2015, methodology): “To investigate the model’s capability of simulating real-world situations, a set of simulations is conducted and a comparison between a simple gap-acceptance based lane-changing model and the MOBIL lane-changing model (Kesting et al., 2007) with the proposed game theory based model is presented.”
Energy saving potentials of connected and automated vehicles (2018, methodology): “There is a large body of literature on lane change models for traffic microsimulations, such as the widely used MOBIL lane change model introduced in [158].”
Imitating driver behavior with generative adversarial networks (2017, methodology): “on and deceleration, preferred free road and bumper-tobumper headways, and realistic braking behavior. Such carfollowing models were later extended to multilane conditions with controllers like MOBIL [4], which maintains a utility function and “politeness parameter” to capture intelligent driver beh”
Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity (2009, methodology): “The lanechanging decisions have been modelled using MOBIL (Kesting et al. 2007), as have the decisions of on-ramp vehicles to join the main flow.”
Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data (2008, methodology): “The acceleration information in the data can be used to investigate to what extent the local traffic environment (consisting, e.g., of the next-nearest and further leading vehicles) inﬂuences longitudinal driving behavior ( 20 ).”