A New Multi-objective Control Design for Autonomous Vehicles

Chunyu, Jiangmin; Qu, Zhihua; Pollak, Eytan; Falash, Mark

doi:10.1007/978-3-540-88063-9_5

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…Coordinated formation methods for multiple autonomous vehicles are well-studied in the literature and can be categorized in three main approaches: Leader-follower, virtual structure, and behavior-based approach. In leader-follower approach the follower agents track the coordinates of the leader [10], [11]. This method is effective for conventional single-lane train-like platoon, but since the follower must follow the same reference trajectory as the leader, it is not applicable to reconfigurable multi-lane platoons, in which the planned motions for the vehicles are not the same.…”

Section: Literature Reviewmentioning

confidence: 99%

Formation and Reconfiguration of Tight Multi-Lane Platoons

Firoozi¹,

Zhang²,

Borrelli³

2020

Preprint

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Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, we present an architecture for autonomous navigation of tight multi-lane platoons travelling on public roads. Using the proposed approach, CAVs are able to form single or multilane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of three main components: an online decision-maker, an offline motion planner and an online path-follower. The decisionmaker selects the desired platoon configuration based on realtime information about the surrounding traffic. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. The motion planner uses a Model Predictive Control scheme to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on the vehicles' control units to be executed by a low-level path-following feedback controller in real-time based on the selected desired configuration. We demonstrate the effectiveness of our approach through simulations of three case studies: 1) formation reconfiguration 2) obstacle avoidance, and 3) benchmarking against behaviorbased planning in which the desired formation is achieved using a sequence of motion primitives. Videos and software can be found online here https://github.com/RoyaFiroozi/ Centralized-Planning. I. INTRODUCTIONVehicular wireless communication systems including vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) enhance cooperative driving by providing a communication network for information exchange between the vehicles to coordinate and plan conflict-free trajectories [1], [2]. Grouping multiple cooperative vehicles into single-lane or multi-lane formation is referred to as platooning. Using communication technologies, connected vehicles within the platoon can navigate in close proximity of each other, selforganize themselves to form certain configurations, keep tight formations and transit from one formation to another. Platooning improves traffic congestion, energy efficiency and safety [3], [4]. It increases road traffic throughput by allowing small inter-vehicle distances. Furthermore, moving with close spacing reduces aerodynamic drag and thus contributes to energy efficiency.Platooning in classical setting refers to a group of vehicles that form a road train in a single lane [5], [6].

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Section: Literature Reviewmentioning

confidence: 99%

Formation and Reconfiguration of Tight Multi-Lane Platoons

Firoozi¹,

Zhang²,

Borrelli³

2020

Preprint

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“…Explicit conditions on how to choose an appropriate composite potential field function are found, and a reactive control is provided for each vehicle to asymptotically track its goal while avoiding static objects, moving obstacles and other vehicles. The multi-objective reactive control design, originally proposed in [42], makes the vehicles pliable to their dynamical surroundings. In Section 6.6, this pliable control will be combined into cooperative control to achieve any cooperative and pliable formation of vehicles in a dynamically changing environment, while the consensus problem of non-holonomic systems will be addressed in Section 6.5.…”

Section: Notes and Summarymentioning

confidence: 99%

Cooperative Control of Dynamical Systems

2009

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“…There are three kinds of leader in this approach, namely static leader, dynamic leader and virtual leader. The advantage of this approach is the reduced tracking errors and can be analysed using standard control techniques [5]. Another benefit is that only the leader is responsible for planning trajectories and followers must follow the coordinates of the leader; therefore, it results in a simple controller.…”

Section: Introductionmentioning

confidence: 99%

“…The behaviour coordinator multiplies the output of each behaviour by its relative weight, then summing and normalizing the results. One advantage of this approach is that it can operate in the unknown and dynamic environment because it is a parallel, real-time and distributed method, requiring less information sharing [5]. Its another advantage is that each behaviour has its physical meaning and the formation feedback can be incorporated into group dynamics by coupling the outputs of each individual behaviour.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, the desired trajectory is not assigned to the single agent, but it is shared by the whole formation team. In terms of advantage, this approach is easy to prescribe the coordinated behaviour for the whole group [5]. In terms of disadvantage, this approach is centralized, therefore, a single point of failure can crash the whole system.…”

Section: Introductionmentioning

confidence: 99%

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Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Soni

2018

Robotics

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Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

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A New Multi-objective Control Design for Autonomous Vehicles

Cited by 8 publications

References 12 publications

Formation and Reconfiguration of Tight Multi-Lane Platoons

Formation and Reconfiguration of Tight Multi-Lane Platoons

Cooperative Control of Dynamical Systems

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

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