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.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode Eleanor and Kerry talk to Pedro Oliveira, a researcher and sound artist based at the Akademie der Künste in Berlin. Pedro does amazing work investigating border control technologies that listen to asylum seekers and claim to be able to discern where they came from from the way they speak. In this episode we discuss why these kinds of technologies rely on the assumption that there is an authentic way that a migrant from a particular place should sound. Our quest to unravel vocal authenticity takes us through frequency, timbre, and 1960s synthesisers from East Berlin.