43 research outputs found
A New Safety Objective for the Calibration of the Intelligent Driver Model
The intelligent driver model (IDM) is one of the most widely used
car-following (CF) models in recent years. The parameters of this model have
been calibrated using real trajectories obtained from naturalistic driving
,driving simulator experiment and drone data. An important aspect of the model
calibration process is defining the main objective of the calibration. This
objective, influences the objective function and the performance measure for
the calibration. For example, to calibrate CF models, the objective is usually
to minimize the error in measured spacing or speed while important safety
aspects of the models such as the collision avoidance mechanisms are ignored.
For such models, there is no guarantee that the calibrated parameters will
preserve the safety properties of the model since they are not explicitly taken
into account. To explicitly account for the safety properties during
calibration, this paper proposes a simple objective function which minimizes
both the error in the actual measured spacing (as it is currently done) and the
error in the dynamic safety spacing (desired minimum gap) derived from the
collision free property of the IDM model. The proposed objective function is
used to calibrate two variants of the IDM using vehicle trajectories obtained
with drone from a Dutch highway. The calibration performance is then compared
in terms of the error in actual spacing and time gap. The results show that the
proposed safety objective 15 function leads to lower errors in spacing and time
gap compared to when minimizing for only spacing and preserves collision
property of the IDM.Comment: To be submitted to the Transportation Research Records Journa