MR-IDM - Merge Reactive Intelligent Driver Model: Towards Enhancing Laterally Aware Car-following Models
2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC(2023)
Abstract
This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world observational data from the merging vehicle's perspective in various traffic conditions ranging from free-flowing to rushhour traffic jams. Next, as our core contribution, we introduce a novel car-following model, called MR-IDM, for highway driving that reacts to merging vehicles in a realistic way. This proposed driving model can either be used in traffic simulators to generate realistic highway driving behavior or integrated into a prediction module for autonomous vehicles attempting to merge onto the highway. We quantitatively evaluated the effectiveness of our model and compared it against several other methods. We show that MR-IDM has the least error in mimicking the real-world data, while having features such as smoothness, stability, and lateral awareness.
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Key words
Car-following Model,Intelligent Driver Model,Real-world Data,Traffic Congestion,Autonomous Vehicles,Traffic Conditions,Traffic Model,Vehicle Behavior,Traffic Simulation,Error Of The Mean,Scaling Factor,Visual Angle,Real-world Datasets,Absolute Distance,Respective Domains,Gap Distance,Decision-making Algorithm,Data Processing Steps,Lane Change,Driver Behavior,Traffic Behavior,Acceleration Model,Controller Area Network,Longitudinal Distance,Data Collection Sites,Positive Acceleration,Speed Profile,Changes In Acceleration
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