Lateral vehicle control is a high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The Linear Quadratic Regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: Artificial Bee Colony Optimization (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as testing platform, the study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the Model-in-the-Loop and the Hardware-in-the-Loop gap up to 78,89% with lateral velocity when we use Relative Error Criterion (REC) method to validate and 2.35m with lateral displacement when considering by maximum absolute value method.

Vehicle safety promises to be one of the Advanced Driver Assistance System (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, Lidar and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. As most of the current study is based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

Road marking recognition is of great importance for the deployment of automated driving systems and for already existing driver line-keeping assistance systems. For proper and reliable function of machine vision systems that include LiDAR and camera, road markings must be visible to them to facilitate planning of driving trajectory. In this study, LiDAR intensity and the number of LiDAR points collected from a field analysis of two road markings systems reflectorised with premium and standard glass beads were analysed. In the lateral direction, it was demonstrated that the use of premium glass beads for road markings lead to 56% higher LiDAR intensity and 14% more LiDAR points captured than in case of standard road markings. In the longitudinal direction, premium road markings showed less distance attenuation, which is seen as advantageous for trajectory planning by machine version systems. The outcomes of this study offer insights for road infrastructure decision-makers, developers of automated driving systems, and researchers in the field of transportation technology.