## 2 Related Work
In addition, vision-based localisation only works during the day, and a camera placed outside the vehicle is susceptible to dirt and damage. Therefore, the production-ready ADAS variant evaluated in the field operational tests reported in this article uses the GNSS receivers and those sensors that are by default mounted in an electric bus. The GNSS is often applied in monitoring vehicles in urban traffic [19; 20]. [PERSON] et al. [21] demonstrated that GNSS can be used for monitoring buses in urban areas. The possibility of using GNSS data to monitor and analyse the behaviour of urban bus drivers, including the detection of unsafe situations, such as speeding or braking sharply, has been demonstrated in [22]. Unfortunately, accurate and reliable localisation based on GNSS data in urban areas is pledged by a number of problems, as there are multiple signal reflections, and the number of satellites visible at the same time is not always sufficient [23]. Therefore, numerous attempts are being made to improve the quality and reliability of positioning vehicles with GNSS solutions [20] and multi-sensory systems [24]. In terrestrial positioning systems, the GNSS integrity concept known from aeronautical navigation is being introduced [25]. However, the integrity monitoring algorithms developed for aeronautical applications assume that a high data redundancy exists, which is not the case for urban GNSS localisation, due to frequent and often large errors caused by multipath and non-line-of-sight (NLOS) signal propagation. Hence, the receiver autonomous integrity monitoring (RAIM) technique that uses only the GNSS pseudorange measurements does not work well in urban environments. Methods employing additional sensors, e.g., omni-directional vision [26], were proposed to monitor the visibility of satellites improving the accuracy of GNSS localisation in urban transport. The reliability of GNSS-based localisation can also be improved by exploiting the redundancy provided by multiple GNSS constellations (GPS, GLONASS, Galileo, Beidou) and multiple frequencies, which are available in modern receiver modules. It is possible to extract raw GNSS measurements (pseudorange, Doppler shift, and carrier phase), using these data to compute a better position estimate of the vehicle, e.g., by modelling accurately the pseudorange errors [27]. A widely explored approach to improve the satellite-based localisation of ground vehicles is to fuse pose estimates from GNSS and vehicle position estimates obtained with an exteroceptive sensor, such as a laser scanner or camera. Early works in this area used 2D laser scanners and Kalman filtering as the fusion framework [28]. Nowadays, factor graph optimisation started to be employed as the fusion mechanism, with [29] demonstrating that the integration of inertial measurements and GNSS using factor graph outperforms Kalman filtering and [30] showing the integration of GNSS and visual SLAM (simultaneous localisation and mapping). Our recent paper [31] introduced the GNSS-aided laser SLAM (GALS), which is a factor graph-based approach to the integration of GNSS measurements and off-the-shelf laser SLAM systems for large-scale urban navigation. Whereas the multi-sensor localisation systems provide an opportunity to substitute the GNSS position estimates whenever the GNSS data become degraded by the limited satellite visibility, multipath effects, or foliage attenuation, these systems are expensive due to the cost of sensors and the increased need for computing power necessary for SLAM algorithms. Hence, the need to achieve a good cost-to-effect ratio in a market-ready system was the main motivation behind our decision to use a commercial RTK GNSS [32] with corrections obtained from the Internet as the localisation solution. However, we are aware of the possible problems with the integrity of GNSS-based position estimates, and thus, we devote part of the evaluation to showing that this part of our ADAS works reliably.