Advanced Driver Assistance Systems (ADAS) are becoming more available and will become mandatory for all new vehicle models from 2022 onward. In order to achieve the highest safety benefits, it is important that these systems are available. Lane Keep Assist (LKA) is part of ADAS and assists the driver in the lateral control of the vehicle. Lane markings are used by both human drivers and machine vision to stay on the road, but factors contributing to lane marking detection in different driving conditions are mostly unknown. A field test was conducted on Dutch provincial roads to evaluate lane marking visibility properties in relation to the LKA detection performance of different sensor types. The LKA detection performance of the mono camera was found to be higher in most weather and illumination conditions than the detection performance of the mono camera with infrared. The mono camera with infrared had a higher detection performance during rain in nighttime conditions than during dry daytime conditions. The highest detection performance for the mono camera and the mono camera with infrared were 97% in dry nighttime conditions and 91,4% in sunset conditions, respectively. Binary logistic regression was used to determine the effect of lane marking properties on the lane detection performance. A profiled lane marking type was found to increase the detection likelihood by 6-8 times as opposed to a smooth lane marking type. Other visibility properties, such as retroreflectivity and contrast with the road surface, were not found to be a significant contributor to the detection performance.

Advancements in technology are bringing Automated vehicles (AVs) closer to reality and it is not so far in future when these vehicles will have a significant penetration rate in our existing transport network. However, in the early phases of automation, penetration rate of these vehicles will be very low. This would lead to a mixed traffic situation where AVs will coexist and interact with other human driven vehicles (HDVs). This one to one interaction might lead to change in driving behavior of HDVs when they interact with AVs. Thus it is crucial to understand the behavioral adaptation of HDVs due to their interaction with AVs. A field test was conducted to gain insights about such behavioral adaptation focusing on gap acceptance, car following, and overtaking behavior. Furthermore, learning effet over multiple interactions with AVs, effect of positive and negative information, stress during interaction and trust in AVs were also studies. This research concludes following findings. First, significantly lower critical gaps were accepted during interaction with AVs. Second, significantly lower headways were found at the end of overtaking during interaction with AVs. Third, higher trust was reported in AV scenarios. Fourth, positive information leaded to even closer interactions with AVs in comparison to HDVs. These findings indicate the potential exploitation of AV technology by HDV-drivers.