Paolo Falcone

Predictive Active Steering Control for Autonomous Vehicle Systems

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

MPC-based approach to active steering for autonomous vehicle systems

In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.

MPC-Based Yaw and Lateral Stabilization Via Active Front Steering and Braking

In this paper, we propose a path following Model Predictive Control-based (MPC) scheme utilising steering and braking. The control objective is to track a desired path for obstacle avoidance manoeuvre, by a combined use of braking and steering. The proposed control scheme relies on the Nonlinear MPC (NMPC) formulation we used in [F. Borrelli, et al., MPC-based approach to active steering for autonomous vehicle systems, Int. J. Veh. Autonomous Syst. 3(2/3/4) (2005), pp. 265–291.] and [P. Falcone, et al., Predictive active steering control for autonomous vehicle systems, IEEE Trans. Control Syst. Technol. 15(3) (2007), pp. 566–580.]. In this work, the NMPC formulation will be used in order to derive two different approaches. The first relies on a full tenth-order vehicle model and has high computational burden. The second approach is based on a simplified bicycle model and has a lower computational complexity compared to the first. The effectiveness of the proposed approaches is demonstrated through simulations and experiments.

Design and Experimental Validation of a Cooperative Driving System in the Grand Cooperative Driving Challenge

In this paper, we present the Cooperative Adaptive Cruise Control (CACC) architecture, which was proposed and implemented by the team from Chalmers University of Technology, Göteborg, Sweden, that joined the Grand Cooperative Driving Challenge (GCDC) in 2011. The proposed CACC architecture consists of the following three main components, which are described in detail: 1) communication; 2) sensor fusion; and 3) control. Both simulation and experimental results are provided, demonstrating that the proposed CACC system can drive within a vehicle platoon while minimizing the inter-vehicle spacing within the allowed range of safety distances, tracking a desired speed profile, and attenuating acceleration shockwaves.

Predictive control approach to autonomous vehicle steering

A model predictive control (MPC) approach to active steering is presented for autonomous vehicle systems. The controller is designed to stabilize a vehicle along a desired path while rejecting wind gusts and fulfilling its physical constraints. Simulation results of a side wind rejection scenario and a double lane change maneuver on slippery surfaces show the benefits of the systematic control methodology used. A trade-off between the vehicle speed and the required preview on the desired path for vehicle stabilization is highlighted

A linear time varying model predictive control approach to the integrated vehicle dynamics control problem in autonomous systems

A Model Predictive Control (MPC) approach for controlling active front steering, active braking and active differentials in an autonomous vehicle is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle, brakes and traction at the four wheels independently, while fulfilling various physical and design constraints. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the system inputs in order to best follow the desired trajectory on slippery roads at a given entry speed. We start from the results presented in [1], [2] and formulate the MPC problem based on successive on-line linearization of the nonlinear vehicle model (LTV MPC). Simulative results are presented, interpreted and compared against LTV MPC schemes which make use only of steering and/or braking.

Predictive Threat Assessment via Reachability Analysis and Set Invariance Theory

We propose two model-based threat assessment methods for semi-autonomous vehicles, i.e., human-driven vehicles with autonomous driving capabilities. Based on information about the surrounding environment, we introduce a set of constraints on the vehicle states, which are satisfied under “safe” driving conditions. Then, we formulate the threat assessment problem as a constraint satisfaction problem. Vehicle and driver mathematical models are used to predict future constraint violation, indicating the possibility of accident or loss of vehicle control, hence, the need to assist the driver. The two proposed methods differ in the models used to predict vehicle motion within the surrounding environment. We demonstrate the proposed methods in a roadway departure application and validate them through experimental data.

A model predictive control approach for combined braking and steering in autonomous vehicles

In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in F. Borrelli et al. (2005) and P. Falcone et al. (2006), where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.

A hierarchical Model Predictive Control framework for autonomous ground vehicles

A hierarchical framework based on Model Predictive Control (MPC) for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle while fulfilling various physical and design constraints. We start from the low-level active steering-controller presented in [3], [9] and integrate it with a high level trajectory planner. At both levels MPC design is used. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model. At the low- level a MPC controller computes the vehicle inputs in order to best follow the desired trajectory based on detailed nonlinear vehicle model. This article presents the approach, the method for implementing it, and successful preliminary simulative results on slippery roads at high entry speed.

Autonomous cooperative driving: A velocity-based negotiation approach for intersection crossing

In this article, a scenario where several vehicles have to coordinate among them in order to cross a traffic intersection is considered. In this case, the control problem relies on the optimization of a cost function while guaranteeing collision avoidance and the satisfaction of local constraints. A decentralized solution is proposed where vehicles sequentially solve local optimization problems allowing them to cross, in a safe way, the intersection. This approach pays a special attention to how the degrees of freedom that each vehicle disposes to avoid a potential collision can be quantified and led to an adequate formalism to the considered problem. In the proposed strategy, collision avoidance is enforced through local state constraints at given time instants and agents are assumed to only communicate the available time to react and the time stamps at which they expect to be within the intersection. Simulations results on the efficiency and performance of the proposed approach are also presented.