In the coming decade, Advanced Driver Assistance Systems (ADAS) will play a crucial role in improving traffic safety within the European Union (EU). Notably, the European Commission mandates that from July 2024, all new vehicles must be equipped with Lane Keeping Assistance (LKA) systems. LKA systems provide force feedback to drivers when vehicles cross lane markings, employing cameras, radar, or LiDAR systems for lane detection.

To optimize LKA performance, this research focuses on understanding the influence of lane marking properties and adverse scenarios on detection ability.

Field Test and Setup:
A field test was conducted at a track in Lelystad, featuring various state-of-the-art lane markings: an old white paint lane marking as a reference, and three new lane markings (two tape types from 3M and one cold spray plast from Triflex). Lane markings were tested under dry and wet conditions.

Data Collection:
Measurements included luminance coefficient (Qd) and retroreflectivity values (Rl for dry and Rw for wet) of lane markings and asphalt, along with contrast ratios. Test runs were conducted during sunset, in complete darkness, and under different scenarios involving oncoming traffic and street lights.

Analysis and Findings:
After analyzing 414 valid runs, key findings were identified:

Lane markings are 3.3 times more likely to be detected in dry versus wet conditions.
Driving towards a light source reduces detection likelihood by 4.5 to 5 times compared to driving away from the light.
Higher wet retroreflectivity (Rw) improves LKA system performance.
Oncoming traffic with main beam headlights reduces detection likelihood significantly.
Bright light sources negatively affect contrast in camera-detected images.
New lane markings outperform old white paint markings by 2.1 to 4.3 times in detection likelihood.

Implications and Recommendations:
The study confirms the importance of lane marking detectability for LKA systems reliant on cameras. Enhancing retroreflection and contrast improves detection ratios, thereby enhancing traffic safety and potentially preventing accidents. Further research is recommended on the impact of oncoming traffic and street lights, including light type and brightness.

For future lane marking developments, testing under adverse conditions with varying light sources and wet surfaces is advised to optimize LKA system performance and ensure safety on the roads.

Lane detection represents a fundamental task for automated/autonomous vehicles. Current lane detection methods do not provide the versatility of real-time performance, robustness,and accuracy required for real-world scenarios. The reasons include lack of computing power while being portable and inability to observe the continuity and structure of lane lines over a sequence of images. An investigation into the present methods in the literature reveals that deep learning networks cannot focus on relevant images and critical parts of the images. The neural networks implemented with max-pooling operations can cause a loss of information at a granular level of the image during the downsampling of images. Obtaining a fixed set of lane locations will restrict the number of lane lines detected. It hinders the generalisability of the network. This thesis aims to introduce a novel spatio temporal method that can focus on lane lines and key features to increase the robustness and accuracy of lane line detection.The spatio temporal attention network based on Long Short Term Memory (LSTM) units tested on the tvtLane dataset provided an accuracy of 98.1443%, the precision of 0.8873, and F1-score of 0.9108. The precision and F1-measure are the highest when compared to state-of-the-art lane detection networks. The spatio temporal FC attention network produces better accuracy and precision on TuSimple dataset than state-of-the-art networks with 98.2078% and 0.8861, respectively. Testing on the LLAMAS dataset, the network achieved an average precision of 0.8028 and corner recall of 0.7183. The results show the robustness and high accuracy on two different datasets with unique distributions. Although the network is trained on datasets with a maximum of five lanes, it can detect more than five lanes in an image.The network is also able to detect lanes on the unseen Netherlands dataset.

Driving involves communicative interactions where human drivers use communication signals to negotiate their right-of-way for road safety. The introduction of automated vehicles (AVs) in mixed-traffic environment, where human drivers will interact with AVs, will affect the nature of these communicative interactions. AVs and human-driven vehicles (HDVs) use different communication forms (e.g., vehicle-to-vehicle communication between AVs vs eye-contact between humans). This raises road safety concerns. AVs might need a communication system that conveys intent to HDVs, clearly. This research investigates the potential of external human machine interfaces (eHMIs) in AV-HDV interactions at T-intersections to improve communicative interactions. Traditional traffic signals were used as inspiration to develop two eHMIs concepts, one placed on vehicle while the other on infrastructure. eHMI on vehicle might require less visual scanning from the interacting human to know AV intent and speed, but on the other hand, the AV might not be in driver field-of-vision compared to an eHMI on the infrastructure. The effects of eHMIs were investigated using a driving simulator with forty-six participants. The results show that both eHMIs had a significant and positive effect on driver trust, acceptance, and emotions. Drivers were calmer with eHMI placed on the infrastructure than on the vehicle. Both eHMIs reduced the time to maximum braking of human drivers and increased their compliance with AVs. The eHMI on vehicle reduced critical interactions, measured by the Post Encroachment Time, between AVs and HDVs. It is concluded that eHMIs can improve AV-HDV communicative interactions at T-intersections. No significant differences were observed between the eHMI conditions in participants’ preference and efficiency of the AV-HDV interactions, measured by human driver compliance. Hence, this research recommends further investigation of eHMIs in different on-road interactions.