On model-based longitudinal feed-forward motion control for low velocities on known road profiles

ABSTRACT

So far, longitudinal motion control has focused on situations like highway driving, where disturbances of the road profile can be neglected. In this paper, we show how the Two Point Tire Model can be used to derive a novel feed-forward control law for a vehicle's longitudinal motion that considers the effects of the road profile and can complement existing control approaches. For this purpose, we recapitulate the basic model assumptions and equations and briefly discuss how it can be used on arbitrary road profiles. Two approaches for implementation in a real vehicle are presented. Comparisons of these approaches in simulation and to a human driver of an experimental vehicle show that the controller can deal with stepped obstacles of up to 14 cm in height. However, the control performance is essentially limited by the actuator delay and human drivers outperform the controller due to their ability of sensing subtle vehicle motions. The results indicate that the control performance can be further improved by using a preview on the necessary drive torque, which can be provided by the solution that we propose.

KEYWORDS:

• Autonomous driving
• vehicle dynamics
• Two Point Tire Model
• road profile disturbances
• longitudinal motion control
• feed-forward acceleration control

Acknowledgments

The authors would like to give thanks to Professor Dr. Dieter Ammon, Dr. Klaus-Peter Kuhn and Thorsten Lajewski, M.Sc., for the vivid discussions about this work and for their active support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Depending on the number of springs and the arrangement of the contact points, these are also the equations of motion for the well-known Point Contact (n=1) and Radial Spring Model (many, equally spaced springs).

2. By relevant, features that have a profound impact on the longitudinal motion are meant. Features that only affect vertical motion or ride comfort are neglected.

3. From that one can also get a new estimate of ${\theta }_{\mathrm{Veh}}$, if necessary.

4. Both the static method and the online simulation can be used to create a simple preview in the sense that they are calculated for a longitudinal position that lies a short distance ahead of the actual vehicle position. This trick will be used with different preview distances in the following section.

5. We chose a setup where the acceleration controller is given a fixed set-point in order to highlight the differences between the presented approaches. Driving with an overlaying velocity controller may be more realistic, but such a controller would also correct some of the deficits of the underlying acceleration controller and thus distort the results and impede comparison.

6. Note, that the quantitative values for the torque requested by the driver are only partially comparable to the controller requested and measured torques. The driver torque is calculated by multiplying the set engine torque with a fixed gear ratio and thus not considering losses in the drive train, for example.