
Controlling autonomous vehicles typically has two main components: planning a trajectory and tracking this trajectory using feedback controllers. To benefit from the recent progress in planning algorithms, it is key that the underlying tracking controller is able to follow the planned trajectory as desired. In emergency situations in particular, it is crucial that feedback controllers steer the vehicle as close as possible to the planned trajectory to remain within a safe corridor. While there exists much work on the design of trajectory and path tracking controllers for vehicles, little work has been done to systematically compare different approaches, especially when considering extreme situations, uncertain parameters, and disturbances. In this work, we compare eight tracking controllers in a systematic way, each of them representing a different controller family. By not only considering nominal behavior, but also sensor noise and uncertain parameters, we obtain for the first time a broad comparison of the behavior of different controllers in various situations.
ddc: ddc:
ddc: ddc:
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 43 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
