2,391 research outputs found
Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach
Research in the field of automated driving has created promising results in
the last years. Some research groups have shown perception systems which are
able to capture even complicated urban scenarios in great detail. Yet, what is
often missing are general-purpose path- or trajectory planners which are not
designed for a specific purpose. In this paper we look at path- and trajectory
planning from an architectural point of view and show how model predictive
frameworks can contribute to generalized path- and trajectory generation
approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles,
CA, US
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
The problem of maneuvering a vehicle through a race course in minimum time
requires computation of both longitudinal (brake and throttle) and lateral
(steering wheel) control inputs. Unfortunately, solving the resulting nonlinear
optimal control problem is typically computationally expensive and infeasible
for real-time trajectory planning. This paper presents an iterative algorithm
that divides the path generation task into two sequential subproblems that are
significantly easier to solve. Given an initial path through the race track,
the algorithm runs a forward-backward integration scheme to determine the
minimum-time longitudinal speed profile, subject to tire friction constraints.
With this fixed speed profile, the algorithm updates the vehicle's path by
solving a convex optimization problem that minimizes the resulting path
curvature while staying within track boundaries and obeying affine,
time-varying vehicle dynamics constraints. This two-step process is repeated
iteratively until the predicted lap time no longer improves. While providing no
guarantees of convergence or a globally optimal solution, the approach performs
very well when validated on the Thunderhill Raceway course in Willows, CA. The
predicted lap time converges after four to five iterations, with each iteration
over the full 4.5 km race course requiring only thirty seconds of computation
time on a laptop computer. The resulting trajectory is experimentally driven at
the race circuit with an autonomous Audi TTS test vehicle, and the resulting
lap time and racing line is comparable to both a nonlinear gradient descent
solution and a trajectory recorded from a professional racecar driver. The
experimental results indicate that the proposed method is a viable option for
online trajectory planning in the near future
A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models
In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.This work has been conducted within the ENABLE-S3 project that has
received funding from the ECSEL Joint Undertaking under Grant Agreement
No 692455. This work was developed at Tecnalia Research & Innovation
facilities supporting this research
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