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The Influence of Autonomous Driving on Passive Vehicle Dynamics

Published April 3, 2018 by SAE International in United States
The Influence of Autonomous Driving on Passive Vehicle Dynamics
Sector:
Citation: Novi, T., Liniger, A., Capitani, R., Fainello, M. et al., "The Influence of Autonomous Driving on Passive Vehicle Dynamics," SAE Int. J. Veh. Dyn., Stab., and NVH 2(4):285-295, 2018, https://doi.org/10.4271/2018-01-0551.
Language: English

Abstract:

Traditional vehicles are designed to be inherently stable. This is typically obtained by imposing a large positive static margin (SM). The main drawbacks of this approach are the resulting understeering behavior of the vehicle and, often, a decrease in peak lateral grip due to oversized rear tire characteristics. On the other hand, a lower SM can cause a greater time delay in the vehicle’s response which hardens the control of a vehicle at limit handling for a human being. By introducing advanced autonomous driving features into future vehicles, the human factor can be excluded in limit handling manoeuvers (e.g., obstacle avoidance occurrences) and, consequently, the need for a high SM (i.e., high controllability for human drivers) can be avoided. Therefore, it could be possible to exploit the passive vehicle dynamics and enhance the performance, both in terms of peak grip and transient response.
The goal of this article is to explore if a decrease in SM can lead to a performance advantage on an obstacle avoidance manoeuver when the vehicle is driven by a robotic controller. This is achieved by analyzing the behavior of various vehicle models with different SMs and peak lateral acceleration on a nonstandard double lane change manoeuver. After having characterized the dynamic response of the various models in both steady-state and unsteady-state, several tests are run on a Driver-in-Motion (DiM) dynamic driving simulator driven by human drivers. The same tests are run again in a Model-in-the-Loop (MiL) simulation where the vehicle is controlled by means of a nonlinear model predictive control (NMPC). The results show that the robotic controller outperforms a human driver and poses interesting design challenges for autonomous vehicles in terms of passive stability and active controllers to modify vehicle stability online.