Abstract-This paper presents a novel linear time-varying model predictive controller (LTV-MPC) using a sparse clothoidbased path description: a LTV-MPCC. Clothoids are used world-wide in road design since they allow smooth driving associated with low jerk values. The formulation of the MPC controller is based on the fact that the path of a vehicle traveling at low speeds defines a segment of clothoids if the steering angle is chosen to vary piecewise linearly. Therefore, we can compute the vehicle motion as clothoid parameters and translate them to vehicle inputs. We present simulation results that demonstrate the ability of the controller to produce a very comfortable and smooth driving while maintaining a tracking accuracy comparable to that of a regular LTV-MPC. While the regular MPC controllers use path descriptions where waypoints are close to each other, our LTV-MPCC has the ability of using paths described by very sparse waypoints. In this case, each pair of waypoints describes a clothoid segment and the cost function minimization is performed in a more efficient way allowing larger prediction distances to be used. This paper also presents a novel algorithm that addresses the problem of path sparsification using a reduced number of clothoid segments. The path sparsification enables a path description using few waypoints with almost no loss of detail. The detail of the reconstruction is an adjustable parameter of the algorithm. The higher the required detail, the more clothoid segments are used.

Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and nonconservative collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method.

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Maneuvering an articulated vehicle on narrow road stretches is often a challenging task for a human driver. Unless the vehicle is accurately steered, parts of the vehicle's bodies may exceed its assigned drive lane, resulting in an increased risk of collision with surrounding traffic. In this work, an optimization-based path-planning algorithm is proposed targeting onroad driving scenarios for articulated vehicles composed of a tractor and a trailer. To this end, we model the tractor-trailer vehicle in a road-aligned coordinate frame suited for on-road planning. Based on driving heuristics, a set of different optimization objectives is proposed, with the overall goal of designing a path planner that computes paths which minimize the off-track of the vehicle bodies swept area, while remaining on the road and avoiding collision with obstacles. The proposed optimization-based path-planning algorithm, together with the different optimization objectives, is evaluated and analyzed in simulations on a set of complicated and practically relevant on-road planning scenarios using the most challenging tractor-trailer dimensions.

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In this paper, a lateral controller is proposed for an over-actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-actuated autonomous research vehicle.

We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth driving of autonomous heavy-duty vehicles, which are characterized by slow actuator dynamics. In order to deal with the slow dynamics, we take into account rate and, additionally, torque limitations of the steering actuator directly. Previous methods only take into account limitations in the path curvature, which deals indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic curvature paths together with circular arcs to steer the vehicle. The obtained paths are characterized by a smooth and continuously differentiable steering angle profile. The final trajectories computed with our method provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by fast computation times. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real-time capabilities.