Accurate vehicle localization is considered to be a key element in future automated driving systems. A network of multiple sensors is employed to deliver information for this localization process. Loosely coupled integration of global navigation satellite systems (GNSS) and inertial navigation systems (INS) data is a common sensor-fusion method for such positioning. One of the problems of this approach, is that exact knowledge of the process- and measurement noise covariance matrices is often not available. The GNSS measurement noise uncertainties, in particular, are highly dynamic and, depending on the specific environment, might follow a non-Gaussian distribution. Since particle filter are known to be superior in non-Gaussian environments, a hybrid filtering variant is proposed: adaptive particle-aided cubature Kalman filtering. This algorithm compromises between a particle filter with kernel density estimation algorithm in periods of non-Gaussian GNSS noise, and a standard cubature Kalman filter in case of Gaussian GNSS noise. The results of GNSS/INS-based localization simulations indicate that the proposed adaptive particle-aided cubature Kalman filter outperforms traditional filtering methods in terms of minimal localization errors.

The operation of automated vehicles in shared areas requires attention with respect to the interaction between AVs and vulnerable road users, including cyclists. Currently, the programmed interaction behavior of AVs is based on the knowledge of the interaction between conventional vehicles and cyclists. However, cyclists may react differently to conventional and automated vehicles. Therefore, this research applies field test experiment to investigate the risks resulting from the interaction between cyclist and an AV. Four possible interaction scenarios were investigated in within-subject design with overtaking speed, overtaking distance and right-hand side objects as attributes. Objective Risk is assessed using the Probabilistic Driving Risk Field and Subjective Risk is assessed based on the self-reported values, cyclist behavior and trust. Results show that in general following has less risk than overtaking. Automated following and manual following have the same level of Objective and Subjective risks, while the automated overtaking has higher risks than manual overtaking. However, results also show that a larger interaction time leads to an increase in cycling speed and decrease in the distance to the curb. Furthermore, in the following maneuver the interaction time is higher than in the overtaking maneuver. Besides high time of interaction, closer overtaking distance and green grass on the right-hand side affect the increase in subjective and objective risks.

The development of automated vehicles offers advantages for the transportation systems of the future. As a result, new and unknown challenges within the field of transportation arise. Moreover, there are uncertainties within the behavioural responses of travellers and amongst other things, the changes in the modal split within the transportation market. There is a lack of extensive knowledge of public transport user preferences regarding automated vehicles. In this study, the relative preferences for a trip with a self-driving bus were compared to a trip with a regular bus. To establish this, a stated preference experiment was conducted. Based on the responses of 282 respondents, a mixed logit model including latent variables was estimated. Based on the estimation results, it can be concluded that public transport users currently show a lower preference for the self-driving bus than for the regular bus. Moreover, travellers’ preferences to travel on the autonomous bus improve when no surveillance is present. Travellers with an increased level of trust are found to perceive more utility of a self-driving bus. This effect is stronger for women, which could explain the outcome that women are less likely to travel by autonomous bus than men. These relative preferences result in the self-driving bus being a competitive alternative for short urban trips, which allow for the increase of the travel costs for the self-driving bus compared to the travel costs for the regular bus. At last, the estimation and application outcomes of this study provide an increased understanding of the stated preferences of public transport users for self-driving vehicles operated as public transport services for urban trips.