278 research outputs found
Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic
Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically
lead to traffic congestion and reduce the travel efficiency of autonomous
vehicles (AVs) in dense traffic. This paper proposes a real-time parallel
trajectory optimization method for the AV to achieve high travel efficiency in
dynamic and congested environments. A spatiotemporal safety module is developed
to facilitate the safe interaction between the AV and SVs in the presence of
trajectory prediction errors resulting from the multi-modal behaviors of the
SVs. By leveraging multiple shooting and constraint transcription, we transform
the trajectory optimization problem into a nonlinear programming problem, which
allows for the use of optimization solvers and parallel computing techniques to
generate multiple feasible trajectories in parallel. Subsequently, these
spatiotemporal trajectories are fed into a multi-objective evaluation module
considering both safety and efficiency objectives, such that the optimal
feasible trajectory corresponding to the optimal target lane can be selected.
The proposed framework is validated through simulations in a dense and
congested driving scenario with multiple uncertain SVs. The results demonstrate
that our method enables the AV to safely navigate through a dense and congested
traffic scenario while achieving high travel efficiency and task accuracy in
real time.Comment: 8 pages, 7 figures, accepted for publication in the 26th IEEE
International Conference on Intelligent Transportation Systems (ITSC 2023
An Optimization-Based Receding Horizon Trajectory Planning Algorithm
This paper presents an optimization-based receding horizon trajectory
planning algorithm for dynamical systems operating in unstructured and
cluttered environments. The proposed approach is a two-step procedure that uses
a motion planning algorithm in a first step to efficiently find a feasible, but
possibly suboptimal, nominal solution to the trajectory planning problem where
in particular the combinatorial aspects of the problem are solved. The
resulting nominal trajectory is then improved in a second optimization-based
receding horizon planning step which performs local trajectory refinement over
a sliding time window. In the second step, the nominal trajectory is used in a
novel way to both represent a terminal manifold and obtain an upper bound on
the cost-to-go online. This enables the possibility to provide theoretical
guarantees in terms of recursive feasibility, objective function value, and
convergence to the desired terminal state. The established theoretical
guarantees and the performance of the proposed algorithm are verified in a set
of challenging trajectory planning scenarios for a truck and trailer system.Comment: Submitted for IFAC World Congress 202
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
Optimal Trajectory Planning for Autonomous Driving Integrating Logical Constraints: An MIQP Perspective
International audienceThis paper considers the problem of optimal trajectory generation for autonomous driving under both continuous and logical constraints. Classical approaches based on continuous optimization formulate the trajectory generation problem as a nonlinear program, in which vehicle dynamics and obstacle avoidance requirements are enforced as nonlinear equality and inequality constraints. In general, gradient-based optimization methods are then used to find the optimal trajectory. However, these methods are ill-suited for logical constraints such as those raised by traffic rules, presence of obstacles and, more generally, to the existence of multiple maneuver variants. We propose a new formulation of the trajectory planning problem as a Mixed-Integer Quadratic Program. This formulation can be solved effectively using widely available solvers, and the resulting trajectory is guaranteed to be globally optimal. We apply our framework to several scenarios that are still widely considered as challenging for autonomous driving, such as obstacle avoidance with multiple maneuver choices, overtaking with oncoming traffic or optimal lane-change decision making. Simulation results demonstrate the effectiveness of our approach and its real-time applicability
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