A Model-Predictive Motion Planner for the IARA autonomous car

Cardoso, Vinicius B.; Oliveira, Josias; Teixeira, Thomas; Badué, Claudine; Mutz, Filipe; Oliveira-Santos, Thiago; Veronese, Lucas; Souza, Alberto F. De

doi:10.1109/icra.2017.7989028

Cited by 29 publications

(10 citation statements)

References 11 publications

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“…For this reason, simplified models are very often preferred. Kinematic bicycle (or singletrack) models [9], [10], [19], or even simpler second-order integrator models [2] are therefore common in the trajectory planning literature.…”

Section: Constrained Second-order Integrator Modelmentioning

confidence: 99%

“…Therefore, the existing literature is generally divided between medium-term (a few seconds) trajectory planning including obstacle avoidance for low-speed applications, mainly relying on simple kinematic models (see, e.g. [9], [10]), and short-term (sub-second) trajectory tracking for high-speed or low-adherence applications using wheel dynamics modeling (see, e.g., [11]- [14]). In the second case, obstacle avoidance is generally not considered (with the notable exception of [15]), and the existence of a feasible collision-free trajectory is not guaranteed in the case of an unexpected obstacle.…”

Section: Introductionmentioning

confidence: 99%

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High-speed trajectory planning for autonomous vehicles using a simple dynamic model

Altché

Polack

Fortelle

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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To improve safety and energy efficiency, autonomous vehicles are expected to drive smoothly in most situations, while maintaining their velocity below a predetermined speed limit. However, some scenarios such as low road adherence or inadequate speed limit may require vehicles to automatically adapt their velocity without external input, while nearing the limits of their dynamic capacities. Many of the existing trajectory planning approaches are incapable of making such adjustments, since they assume a feasible velocity reference is given. Moreover, near-limits trajectory planning often implies high-complexity dynamic vehicle models, making computations difficult. In this article, we use a simple dynamic model derived from numerical simulations to design a trajectory planner for high-speed driving of an autonomous vehicle based on model predictive control. Unlike existing techniques, our formulation includes the selection of a feasible velocity to track a predetermined path while avoiding obstacles. Simulation results on a highly precise vehicle model show that our approach can be used in real-time to provide feasible trajectories that can be tracked using a simple control architecture. Moreover, the use of our simplified model makes the planner more robust and yields better trajectories compared to kinematic models commonly used in trajectory planning.

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Section: Constrained Second-order Integrator Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

Altché

Polack

Fortelle

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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“…For example, some outstanding companies, such as Tesla, Google and Baidu, have successfully deployed autonomous cars in cities, and these cars are expected to be deployed in suburbs of city or rural areas. On top of achievements from leading corporations, numerous autonomous vehicle schemes from universities or individuals, either motor-based schemes [3][4][5][6][7][8][9][10][11] or electronic-based schemes [12][13][14][15][16][17][18][19], are also extraordinary and have decent performance in various of situations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Design and Implementation of Autonomous Robotic Car Based on ROS in Indoor Scenario

et al. 2020

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Pervasive deployment of autonomous vehicle all over the world is an undisputed trend in the future. Autonomous vehicle will inevitably play an essential role in decreasing traffic jams, reducing threats from driving while intoxicated (DWI), and assisting the handicapped to get around. At the same time, the new energy vehicles (NEV) especially the electromobile is gradually adopted by several governments like Germany, USA and China as compulsive transportation tools from the standpoint of environmental friendliness. Taking these two crucial trends into consideration, this article proposes a scheme of autonomous robotic car based on robot operation system (ROS) in electromobile-like car which can be easily transplanted to commercial electromobile. In this article, the design and implementation of robotic car are demonstrated in detail which involves overall architecture of functional modules, hardware design, obstacle avoidance, localization and mapping, land detection and tracking, velocity control and indoor navigation. All software modules and hardware are integrated in NVIDIA Jetson TX1 and TRAXXAS car.

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“…IARA's hardware is composed of a Ford Scape Hybrid retrofitted with an assortment of sensors and processing units. Its software is composed of many modules, which includes a mapper [16], a localizer [17], a path planner, a behavior selector, a motion planner [18], an obstacle avoider [19], and a controller [20]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks

Azevedo

Panceri

Guidolini

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car. The DCNN receives as input (i) an image, which is captured by a camera installed on IARA's roof; and (ii) crops of the image, which are centered in the waypoints computed by IARA's path planner and whose sizes increase with the distance from IARA. We employ an overlap filter to discard detections of the same car in different crops of the same image based on the percentage of overlap of detections' bounding boxes. We evaluated the performance of the proposed augmented-range vehicle detection system (ARVDS) using the hardware and software infrastructure available in the IARA self-driving car. Using IARA, we captured thousands of images of real traffic situations containing cars in a long range. Experimental results show that ARVDS increases the Average Precision (AP) of long range car detection from 29.51% (using a single whole image) to 63.15%.

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A Model-Predictive Motion Planner for the IARA autonomous car

Cited by 29 publications

References 11 publications

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

A Novel Design and Implementation of Autonomous Robotic Car Based on ROS in Indoor Scenario

Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks

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