Safe and Reliable Path Planning for the Autonomous Vehicle Verdino

2016 ◽  
Vol 8 (2) ◽  
pp. 22-32 ◽  
Author(s):  
Rafael Arnay ◽  
Nestor Morales ◽  
Antonio Morell ◽  
Javier Hernandez-Aceituno ◽  
Daniel Perea ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Vehicle ◽  
Reliable Path

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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