Adapted Intelligent Driver Model for Improved Vehicle Following and Assessment of Energy Impacts

Central to predicting the impacts of individual vehicle operations within microscopic traffic simulation is the driver model. A driver model determines a vehicle’s velocity profile in various driving scenarios and interactions with other vehicles. Characteristics including driver behavior and interactions with stop signs, traffic signals, and with a lead vehicle can be modeled and assessed with a representative driver model.

This paper presents the application of an existing intelligent driver model (IDM) with an adaptation for vehicle following dynamics and the interaction with the lead vehicle to be more representative of driver assist systems concerning the relative distance between the lead and simulated ego vehicle. The method uses an additional control term to augment the existing IDM and reduce the inter-vehicle distance to the time gap. The impact on vehicle dynamics is compared and validated with real-world ego vehicle data recorded through driver-assist systems.

The adapted IDM is then employed to simulate real-world driving for an ego vehicle and assess the impact on energy consumption of the ego vehicle using a Reduced Order Energy Model (ROE).