In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well.

The representation of objects in LiDAR point clouds is changed as the height of the mounting position of sensor devices gets increased. Most of the available open datasets for training machine learning based object detectors are generated with vehicle top mounted sensors, thus the detectors trained on such datasets perform weaker when the sensor is observing the scene from a significantly higher viewpoint (e.g. infrastructure sensor). In this paper a novel Automatic Label Injection method is proposed to label the objects in the point cloud of the high-mounted infrastructure LiDAR sensor based on the output of a well performing “trainer” detector deployed at optimal height while considering the uncertainties caused by various factors described in detail throughout the paper. The proposed automatic labeling approach has been validated on a small scale sensor setup in a real-world traffic scenario where accurate differential GNSS reference data where also available for each test vehicle. Furthermore, the concept of a distributed multi-sensor system covering a larger area aimed for automatic dataset generation is also presented. It is shown that a machine learning based detector trained on differential GNSS-based training dataset performs very similarly to the detector retrained on a dataset generated by the proposed Automatic Label Injection technique. According to our results a significant increase in the maximum detection range can be achieved by retraining the detector on viewpoint specific data generated fully automatically by the proposed label injection technique compared to a detector trained on vehicle top mounted sensor data.

Future transportation is expected to be connected, cooperative, and highly automated. Classic automated vehicular functions are developed based on safety principles primarily, but the reliability and cybersecurity of wireless communication processes pose new challenges for the automotive industry for connected vehicles, representing Connected, Cooperative and Automated Mobility (CCAM) systems as cyber-physical systems. Since CCAM system safety is significantly affected by network performance metrics, we need to consider communication factors such as end-to-end latency and packet delivery ratio during the safety evaluation of highly automated vehicle functions. This study presents a methodological framework which links the safety of the CCAM systems with the cyber security sensitive network performance metrics, as well as the vehicle dynamics factors in order to evaluate the effect of intentional or unintentional communication failures. Accordingly, we introduced a new approach to characterize the safety risk of specific Vehicle-to-Vehicle (V2V) applications. Beyond this, severity of collision and message reception probability functions were introduced to further investigate the safety of the analyzed application. Finally, the values of the safety risk index for different warning intervals are summed, which suggest that the safety effect of specific function-related parameters could be and shall be represented during the function development process.