On steering wobble oscillations of motorcycles

Published versio

On the handling performance of a vehicle with different front-to-rear wheel torque distributions

The handling characteristic is a classical topic of vehicle dynamics. Usually, vehicle handling is studied through the analysis of the understeer coe�cient in quasi-steady-state maneuvers. In this paper, experimental tests are performed on an electric vehicle with four independent mo- tors, which is able to reproduce front-wheel-drive, rear-wheel-drive and all-wheel-drive (FWD, RWD and AWD, respectively) architectures. The handling characteristics of each architecture are inferred through classical and new concepts. More speci�cally, the study presents a pro- cedure to compute the longitudinal and lateral tire forces, which is based on a �rst estimate and a subsequent correction of the tire forces that guarantee the equilibrium. A yaw moment analysis is then performed to identify the contributions of the longitudinal and lateral forces. The results show a good agreement between the classical and new formulations of the un- dersteer coe�cient, and allow to infer a relationship between the understeer coe�cient and the yaw moment analysis. The handling characteristics for the considered maneuvers vary with the vehicle speed and front-to-rear wheel torque distribution. In particular, an apparently surprising result arises at low speed, where the RWD architecture is the most understeering con�guration. This outcome is discussed through the yaw moment analysis, highlighting the yaw moment caused by the longitudinal forces of the front tires, which is signi�cant for high values of lateral acceleration and steering angle

Vehicle sideslip estimation for four-wheel-steering vehicles using a particle filter

The availability of the most relevant vehicle states is crucial for the development of advanced vehicle control systems and driver assistance systems. Specifically the vehicle sideslip angle plays a key role, yet this state is unpractical to measure and still not straightforward to estimate. This paper investigates a particle filter approach to estimate the chassis sideslip angle of road vehicles. The filter relies on a physical model of the vehicle and on measurements available from cheap and widespread sensors including inertial measurement unit and steering wheel angle sensor(s). The approach is validated using experimental data collected with the research platform RoboMobil (RoMo), a by-wire electric vehicle with wheel-individual traction and steering actuators. Results show that the performance of the proposed particle filter is satisfactory, and indicate directions for further improvement

Search-Based Motion Planning for Performance Autonomous Driving

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.Comment: Accepted to IAVSD 201