Workpackages in FTM

1. Motion Control (Contact: Baha Zarrouki, M.Sc.) 

   Within the scope of autonomous driving, the truck's longitudinal and lateral motion control to follow the planned trajectory is challenging. To illustrate the control challenges within the ATLAS-L4 Operational Design Domain (ODD), the tractor-trailer system has a limited time window to merge into one highway lane due to the merging lane longitudinal limits and the presence of other road participants. Disturbances and parameter uncertainties such as crosswind, longitudinal- and lateral road banking, and varying trailer load induce strong system state prediction deviations. Hence, the controller can fail to meet the tight time constraints of the reference trajectory, which can lead to safety-critical situations. Thus, we want to achieve a robust control performance.
    

2. Tire-Road Friction Estimation (Contact: Sven Goblirsch, M.Sc.) 

   Knowledge about the maximum transferable force between tire and road is key to safely operate an autonomous vehicle. The vehicle specific dynamic limits must never be exceeded even under highly challenging conditions, e.g. low friction scenarios or emergency trajectories such as maximum deceleration or evasive maneuvers. Doing so would lead to a loss of control over the vehicle. To estimate the limit of the tire-road friction potential cause- and effect-based methods are applied, compared and in a further step combined to decrease the uncertainty of the prediction and thereby increase the robustness of the estimation.

3. Remote-Assistance on Perception-Level (Ansprechpartner: Tobias Kerbl, M.Sc.)                                                                                                                                                                                                                                                                                                                                 Autonomous vehicles (AVs) are confronted with a multitude of challenges in the form of unusual and unforeseeable scenarios, known as edge cases, which can include changing weather conditions, unconventional road layouts, and unanticipated obstacles. Due to the vast number of potential edge cases and their inherent complexity, it is unlikely that all of them can be accounted for during the development of AVs. As a result, it is inevitable that AVs will encounter edge cases during their operation on public roads. To address edge cases during the operation of AVs, teleoperation can provide a fallback solution by enabling a human operator to remotely control (remote-driving) or assist (remote-assistance) the AV. One viable approach for remote-assistance of AVs is to interaction with its perception and interpretation of the surrounding environment. The operator can provide support to the AV by classifying objects, disregarding false-positive detections, or adjusting the predicted behavior of other road users. Given the additional information from the operator, the AV is enabled to continue its mission autonomously.