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.

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.

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.