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Special Issue "Smooth Motion Planning for Autonomous Vehicles"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Dr. Jorge Godoy
E-Mail Website
Guest Editor
Centre for Automation and Robotics (UPM-CSIC), Spanish National Research Council, 28500 Madrid, Spain
Interests: intelligent transportation systems (ITS); artificial intelligence; advanced driver assistance systems (ADAS); V2X communication
Dr. Antonio Artuñedo
E-Mail Website
Guest Editor
Centre for Automation and Robotics (UPM-CSIC), Spanish National Research Council, 28500 Madrid, Spain
Interests: motion planning; decision-making systems; intelligent transportation systems (ITS); artificial intelligence
Dr. Jorge Villagra
E-Mail Website
Guest Editor
Centre for Automation and Robotics (UPM-CSIC), Spanish National Research Council, 28500 Madrid, Spain
Interests: intelligent transportation systems (ITS); decision-making systems; control systems

Special Issue Information

Dear Colleagues,

Interest in autonomous vehicles has significantly increased in recent years—so much so, in fact, that most of the current activity in research and development work on intelligent transportation systems is focused on them. Although huge efforts have been carried out in last years to solve several technological challenges, however, some issues still remain that must be addressed before autonomous vehicles can be extensively deployed in any environment. Among the technologies involved in autonomous navigation, motion planning plays a key role in improving safety and comfort. With the aim of reaching human-level abstract reasoning and reacting safely in a large variety of environments, autonomous vehicles require motion planning methods to generalize unpredictable situations and reason in a timely manner. In addition to that, smooth behavior is also required to guarantee either the occupant’s comfort in autonomous passenger vehicles or a trade-off between efficiency and predictability in unmanned platforms.

The complexity of the scenarios also involves computationally intensive tasks for decision-making and planning methods. Taking into account the limited computational resources available in the on-board computers of a vehicle, which are shared among different tasks (e.g., perception, localization, control), the reduction on the computational resources needed for motion planning becomes critical. In this context, new advances in artificial intelligence and parallel computing can be applied to provide benefits on motion planning strategies.

The purpose of this Special Issue is to present and discuss major research challenges, latest developments, and recent advances on smooth motion planning algorithms applied to autonomous vehicles: underwater or surface vehicles, unmanned ground and aerial vehicles, on/off road vehicles, etc.

The Special Issue topics include but are not limited to the following:

  • Novel path planning techniques for autonomous vehicles;
  • Methods for smooth path and speed planning;
  • Evolutionary algorithms for motion planning;
  • Machine learning methods for motion planning;
  • Motion planning via imitation learning;
  • Methods combining smooth planning and control;
  • Interplay between decision-making, behavior planning and motion planning;
  • Human factors studies related to motion planning;
  • Parallel computing for motion planning;
  • Uncertainty management in motion planning;
  • Motion planning applications.

Dr. Jorge Godoy
Dr. Antonio Artuñedo
Dr. Jorge Villagra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Autonomous vehicles Connected vehicles
  • Autonomous navigation
  • Path planning
  • Speed planning
  • Smooth motion planning
  • Motion planning
  • Obstacle avoidance
  • Maneuver planning
  • Behavior planning
  • Artificial intelligence for motion planning

Published Papers (6 papers)

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Research

Article
Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments
Sensors 2021, 21(13), 4414; https://doi.org/10.3390/s21134414 - 28 Jun 2021
Viewed by 457
Abstract
Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up [...] Read more.
Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Article
How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control
Sensors 2021, 21(12), 4012; https://doi.org/10.3390/s21124012 - 10 Jun 2021
Viewed by 447
Abstract
Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. [...] Read more.
Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Article
Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios
Sensors 2021, 21(11), 3755; https://doi.org/10.3390/s21113755 - 28 May 2021
Viewed by 604
Abstract
Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories [...] Read more.
Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Article
Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies
Sensors 2021, 21(8), 2626; https://doi.org/10.3390/s21082626 - 08 Apr 2021
Cited by 1 | Viewed by 467
Abstract
Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among [...] Read more.
Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Article
A Hybrid Planning Approach Based on MPC and Parametric Curves for Overtaking Maneuvers
Sensors 2021, 21(2), 595; https://doi.org/10.3390/s21020595 - 15 Jan 2021
Cited by 3 | Viewed by 668
Abstract
Automated Driving Systems (ADS) have received a considerable amount of attention in the last few decades, as part of the Intelligent Transportation Systems (ITS) field. However, this technology still lacks total automation capacities while keeping driving comfort and safety under risky scenarios, for [...] Read more.
Automated Driving Systems (ADS) have received a considerable amount of attention in the last few decades, as part of the Intelligent Transportation Systems (ITS) field. However, this technology still lacks total automation capacities while keeping driving comfort and safety under risky scenarios, for example, overtaking, obstacle avoidance, or lane changing. Consequently, this work presents a novel method to resolve the obstacle avoidance and overtaking problems named Hybrid Planning. This solution combines the passenger’s comfort associated with the smoothness of Bézier curves and the reliable capacities of Model Predictive Control (MPC) to react against unexpected conditions, such as obstacles on the lane, overtaking and lane-change based maneuvers. A decoupled linear-model was used for the MPC formulation to ensure short computation times. The obstacles and other vehicles’ information are obtained via V2X (vehicle communications). The tests were performed in an automated Renault Twizy vehicle and they have shown good performance under complex scenarios involving static and moving obstacles at a maximum speed of 60 kph. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Article
Dataset Construction from Naturalistic Driving in Roundabouts
Sensors 2020, 20(24), 7151; https://doi.org/10.3390/s20247151 - 13 Dec 2020
Cited by 2 | Viewed by 741
Abstract
A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes it open [...] Read more.
A proper driver characterization in complex environments using computational techniques depends on the richness and variety of data obtained from naturalistic driving. The present article proposes the construction of a dataset from naturalistic driving specific to maneuvers in roundabouts and makes it open and available to the scientific community for performing their own studies. The dataset is a combination of data gathered from on-board instrumentation and data obtained from the post-processing of maps as well as recorded videos. The approach proposed in this paper consists of handling roundabouts as a stretch of road that includes 100 m before the entrance, the internal part, and 100 m after the exit. This stretch of road is then spatially sampled in small sections to which data are associated. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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