This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified model and the actual vehicle, a separate controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle are controlled using the inverse dynamics of the vehicle powertrain model, and the lateral dynamics are controlled using a linear quadratic regulator. In the non-linear model predictive framework, to obtain safe trajectories, local obstacle information is incorporated into the performance index using a parallax-based method. Simulation results on a full non-linear vehicle model show that the proposed combination of model-predictive-control-based trajectory generation and tracking controller gives satisfactory online obstacle avoidance performance. The paper is structured as follows. Section 2 describes the layout of the overall control structure. Section 3 formulates the MPC framework which is used to generate safe trajectories for the vehicle. Section 4 shows the design procedures of the longitudinal and lateral controllers for tracking the generated trajectory. Section 5 discusses simulation results. Conclusions are presented in section 6.
Obstacle avoidance of autonomous vehicles based on model predictive control
2009
18 Seiten, 17 Bilder, 3 Tabellen, 22 Quellen
Aufsatz (Zeitschrift)
Englisch
Obstacle avoidance of autonomous vehicles based on model predictive control
SAGE Publications | 2009
|Obstacle avoidance of autonomous vehicles based on model predictive control
Online Contents | 2009
|Obstacle avoidance of autonomous vehicles based on model predictive control
Kraftfahrwesen | 2009
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