Proceedings ArticleDOI
Automatic collision avoidance using model-predictive online optimization
Moritz Werling,Darren Liccardo +1 more
- pp 6309-6314
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TLDR
An obstacle avoidance algorithm that simultaneously optimizes steering and braking is proposed that embarks on the nonlinear model-predictive control (NMPC) paradigm, which is capable of solving the optimization problem online.Abstract:
In many traffic emergency situations a collision cannot be prevented by braking alone. Therefore, we propose an obstacle avoidance algorithm that simultaneously optimizes steering and braking. As an emergency scenario approaches the driving limits, a strong nonlinear constraint between braking and cornering develops, suggesting the formulation of a nonlinear constrained online optimization. On this account the proposed algorithm embarks on the nonlinear model-predictive control (NMPC) paradigm, which is capable of solving the optimization problem online. The performance of the algorithm is demonstrated in a simulated pedestrian collision avoidance scenario.read more
Citations
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Journal ArticleDOI
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Abstract: Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.
Journal ArticleDOI
Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC
TL;DR: A trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-Varying system constraints in a receding horizon fashion.
Journal ArticleDOI
Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control
TL;DR: Compared to the previous MPC, which can only be reduced to a nonlinear programming problem, the control sequences of CQP-based MPC can be obtained quickly with improved real-time system performance.
Proceedings ArticleDOI
Fail-safe motion planning of autonomous vehicles
Silvia Magdici,Matthias Althoff +1 more
TL;DR: A fail-safe motion planner is developed, which generates optimal trajectories, yet guarantees safety at all times, by maintaining an emergency maneuver which can safely bring the host vehicle to a stop while avoiding any collision.
Proceedings ArticleDOI
Ensuring drivability of planned motions using formal methods
TL;DR: This work modeling vehicles as differential inclusions composed of simple dynamics and set-based uncertainty is used to provide the set of solutions for these uncertain models and enables safety by checking their mutual non-intersection for consecutive time intervals.
References
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BookDOI
Springer Handbook of Robotics
Bruno Siciliano,Oussama Khatib +1 more
TL;DR: The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications.
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Journal ArticleDOI
Tutorial overview of model predictive control
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Book ChapterDOI
Efficient Numerical Methods for Nonlinear MPC and Moving Horizon Estimation
TL;DR: In this article, numerical methods for solving real-time optimization problems in nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) have been reviewed, focusing exclusively on a discrete time setting.
Journal ArticleDOI
A Real-Time Iteration Scheme for Nonlinear Optimization in Optimal Feedback Control
TL;DR: The robustness and excellent real-time performance of the method is demonstrated in a numerical experiment, the control of an unstable system, namely, an airborne kite that shall fly loops.