Autonomous Vehicle Control: A Nonconvex Approach for Obstacle Avoidance

Rosolia, Ugo; Bruyne, Stijn De; Alleyne, Andrew G.

doi:10.1109/tcst.2016.2569468

Cited by 143 publications

(87 citation statements)

References 25 publications

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“…For polyhedral obstacles, disjunctive programming can be used to ensure collision avoidance, which is often reformulated as a mixed-integer optimization problem [1,4,13]. In case of ellipsoidal obstacles, the collision avoidance constraints can be formulated as a smooth non-convex constraint [37,38], and the resulting optimization problem can be solved using generic non-linear programming solvers.…”

Section: Related Workmentioning

confidence: 99%

Optimization-Based Collision Avoidance

Zhang

Liniger

Borrelli

2021

IEEE Trans. Contr. Syst. Technol.

215

147

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This paper presents a novel method for reformulating non-differentiable collision avoidance constraints into smooth nonlinear constraints using strong duality of convex optimization. We focus on a controlled object whose goal is to avoid obstacles while moving in an n-dimensional space. The proposed reformulation does not introduce approximations, and applies to general obstacles and controlled objects that can be represented as the union of convex sets. We connect our results with the notion of signed distance, which is widely used in traditional trajectory generation algorithms. Our method can be applied to generic navigation and trajectory planning tasks, and the smoothness property allows the use of general-purpose gradient-and Hessian-based optimization algorithms. Finally, in case a collision cannot be avoided, our framework allows us to find "leastintrusive" trajectories, measured in terms of penetration. We demonstrate the efficacy of our framework on a quadcopter navigation and automated parking problem, and our numerical experiments suggest that the proposed methods enable real-time optimization-based trajectory planning problems in tight environments.Source code of our implementation is provided at https://github.com/XiaojingGeorgeZhang/OBCA.

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Section: Related Workmentioning

confidence: 99%

Optimization-Based Collision Avoidance

Zhang

Liniger

Borrelli

2021

IEEE Trans. Contr. Syst. Technol.

215

147

View full text Add to dashboard Cite

show abstract

“…Optimization based methods take advantage of the rigorous theoretical framework of model predictive control (MPC). An example of this approach is [10], where an MPC problem is formulated in a curvilinear coordinate frame, thus exploiting the structure of the planning hierarchy by solving the problem locally around a path. Optimization based approaches require obstacles to be captured in convex sets prior to the optimization, as is demonstrated in [5].…”

Section: B Trajectory Planningmentioning

confidence: 99%

Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

Svensson

Masson

Mohan

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Highly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented.

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“…Rafaila et al [36] and Koga et al [37] also did similar work. Rosolia et al [38] designed a two-stage nonlinear nonconvex control approach, including an outer-loop nonlinear MPC and a model based on an inner linear feedback controller, for autonomous vehicles driving during highway cruise conditions. Beaucorps et al [39] proposed a human-like decision-making algorithm for simple intersections without signals in the presence of another vehicle.…”

Section: Autonomous Driving Studymentioning

confidence: 99%

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

et al. 2018

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With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.

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Autonomous Vehicle Control: A Nonconvex Approach for Obstacle Avoidance

Cited by 143 publications

References 25 publications

Optimization-Based Collision Avoidance

Optimization-Based Collision Avoidance

Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

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