scholarly journals Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3755
Author(s):  
Juan Medina-Lee ◽  
Antonio Artuñedo ◽  
Jorge Godoy ◽  
Jorge Villagra
Keyword(s):  
Motion Planning ◽  
Open Problem ◽  
Autonomous Vehicles ◽  
Merit Function ◽  
Urban Environments ◽  
Use Case ◽  
Rule Based ◽  
Planning Algorithm ◽  
Comfort Parameters ◽  
Independent Motion

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.

scholarly journals Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling

2020 ◽  
Vol 10 (9) ◽  
pp. 3180 ◽  
Author(s):  
Dongfang Dang ◽  
Feng Gao ◽  
Qiuxia Hu
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamical Model ◽  
Planning Problem ◽  
Original Design ◽  
Automatic Driving ◽  
Wide Range ◽  
Planning Algorithm ◽  
Dynamical Coupling

Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.

Predictive Motion Planning for Autonomous Vehicles With Geometric Constraints via Convex Optimization

2020 ◽  
Author(s):  
Xiaoyuan Zhu ◽  
Jian Chen ◽  
Yan Ma ◽  
Jianqiang Deng ◽  
Yuexuan Wang
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Autonomous Vehicles ◽  
Linear Constraints ◽  
Geometric Constraints ◽  
Dynamic Constraints ◽  
The Road ◽  
Moving Obstacles ◽  
Planning Algorithm ◽  
Decision Module

Abstract In this paper, we propose an MPC-based motion planning algorithm, including a decision-making module, an obstacle-constraints generating module, and an MPC-based planning module. The designed decision module effectively distinguishes between structured and unstructured roads and processes them separately, so that the algorithm is more robust in different environments. Besides, the movement of obstacles is considered in the decision-making and obstacle constraints generating module. By processing obstacles with lateral and longitudinal speed separately, obstacle avoidance can be done in scenarios with moving obstacles, including moving obstacles crossing the road. Instead of treating the vehicle as a mass point, we explicitly consider the geometric constraints by modeling the vehicle as three intersecting circles when generating obstacle constraints. This ensures that the vehicle is collision-free in motion planning, especially when the vehicle turns. For non-convex obstacle constraints, we propose an algorithm that generates up to two alternative linear constraints to convexify the obstacle constraints for improving computational efficiency. In MPC, we consider the vehicle kino-dynamic constraints and two generated linear constraints. Therefore, the proposed method can achieve better real-time performance and can be applied to more complicated traffic scenarios with moving obstacles. Simulation results in three different scenarios show that motion planning can achieve satisfactory performance in both structured and unstructured roads with moving obstacles.

scholarly journals An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

2021 ◽  
Vol 10 (3) ◽  
pp. 687
Author(s):  
Naitik Nakrani ◽  
Maulin M. Joshi
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Intelligent Design ◽  
Autonomous Vehicle ◽  
Planning Techniques ◽  
Vehicle Localization ◽  
Adaptive Motion ◽  
Planning Algorithm

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

scholarly journals Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020 ◽  
Vol 10 (21) ◽  
pp. 7716
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Low Complexity ◽  
The Real ◽  
Time Motion ◽  
Planning Algorithm ◽  
Vehicle Motion ◽  
Vehicle Trajectory

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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