Advanced Driver Assistance Systems (ADAS) have been among the key innovations in the automotive market for over a decade, since they promote traffic safety. This tendency is strengthened even more lately, with the introduction of the autonomous vehicles. A plethora of ADAS exist in the market today, using common warning thresholds for all drivers. However, since we are not all driving the same way, by offering common systems for all the drivers, neither the acceptance nor the effectiveness levels of ADAS are optimal. This manuscript attempts to optimize the Collision Avoidance System (CAS) warning, through intelligent personalized algorithms. Starting with the identification of the dynamic parameters for driving behaviour modeling on the longitudinal road axis, the personalization parameters for ADAS are derived that form the basis for the algorithms developed. Also, based on literature studies, the safety boundaries for warning provision by the CAS are set and implemented in the algorithms. Specific personalized algorithms for the longitudinal road axis behaviour are developed, based on Time to Collision and Time Headway. The proposed algorithms based on Time Headway were assessed on-road with 10 drivers and were positively evaluated by the majority of the participants, with a varying degree of reliability and usability. Based on the results obtained, it can be concluded that with the proposed algorithms, the initial hypothesis of the paper is verified, i.e. personalised warnings would get a greater acceptance by the drivers, of course without braking the safety limits. Further improvements of the algorithm could be achieved, possibly through a better determination of the car following event, since its definition includes a few assumptions.

The overall crash statistics in the EU still show a very significant number of car–cyclist crashes. Within the Horizon 2020 project Safe-Up, countermeasures have been developed to reduce this number. One of these countermeasures involves a V2X-enhanced on-board unit for cycles, which can provide on-time warning triggers. The research assumption was based on studying the benefits of connectivity in enhancing cyclists’ safety. This study assessed the performance of this potential technology both qualitatively by analyzing volunteer feedback during physical testing and quantitatively by virtual simulations. The volunteers’ study showed positive findings on system’s safety relevance, user experience, and user acceptance. The method applied for the virtual simulation is a prospective safety performance assessment with reconstructed accident scenarios based on the GIDAS database and cyclist behavior models, obtained from physical testing. The results using a warning trigger 4 s prior to the collision showed a potential safety benefit of approximately 98%. It should be noted that this trigger time was found to be quite early in both physical testing and virtual simulation. Further research is required to evaluate the system’s performance in more complex urban scenarios, as well as to design the human–machine interaction strategies for optimal accident avoidance.

Studies about the use of internet-based services and especially those that are related to services on-the-move, reveal that their propagation is rapidly expanding, with high increase probability in the next years. It is about services that are offered through mobile devices (PDAs, mobile phones) where the travelers wish to receive geographically updated information. The present paper aims at the optimization of public transport (PT) information services that are provided on mobile devices, for travelers of PT means, through their personalization. A specific innovative algorithm is proposed along with the necessary parameters (dynamic and semi-dynamic) that support a holistic personalization. The algorithm is based on each user specific profile, based on the history of his/her previous selections. The proposed algorithm constitutes a ready service, that can be adopted by the appropriate stakeholders in cooperation with content providers such as public transport route providers and dynamic digital maps. The developed algorithm was tested by 10 users. In general, it was positively evaluated by the majority of the users, with a varying acceptance and confidence levels. Users evaluated also the personalization parameters, giving to the maximum walking distance between PT means the highest score. The innovation and limitations of the current algorithm are discussed, followed by proposals for extensions and further research.