Decentralized Cooperative Collision Avoidance for Automated Vehicles: a Real-World Implementation

2020 ◽  
Author(s):  
Daniele Bellan ◽  
Charles Wartnaby
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Automated Vehicles

Automated Vehicles from Modeling to Real World

2015 ◽  
pp. 187-191 ◽  
Author(s):  
Ismail Zohdy ◽  
Raj Kamalanathsharma ◽  
Sudharson Sundararajan ◽  
Ram Kandarpa
Keyword(s):  
Real World ◽  
Automated Vehicles

Collision Avoidance for a Multiple-DOF Manipulator Based on Empty Space Analysis of the 3-D Real World

1992 ◽  
Vol 4 (5) ◽  
pp. 430-436 ◽  
Author(s):  
Hiromu Onda ◽  
◽  
Tsutomu Hasegawa ◽  
Toshihiro Matsui ◽  
Keyword(s):  
Collision Avoidance ◽  
Real World ◽  
Degrees Of Freedom ◽  
Empty Space ◽  
Joint Space ◽  
Degree Of Freedom ◽  
New Method ◽  
Promising Direction ◽  
Space Analysis ◽  
Path Search

This paper describes a new method for finding collisionfree paths for a multiple-degree of freedom (DOF) manipulator with rotational joints and a grasped object. The method first analyzes the structure of empty space in the 3-D workspace. Based on this space analysis, the path search is divided and direction which appears to be most promising is determined in the 3-D workspace. Finally, the path search is systematically executed in the joint space in the direction equivalent to the promising direction. This method is applicable to various problems regardless of the number of degrees of freedom of the manipulator, its structure, and the presence of a grasped object.

scholarly journals Coupling SUMO with a Motion Planning Framework for Automated Vehicles

10.29007/1p2d ◽  
2019 ◽  
Author(s):  
Moritz Klischat ◽  
Octav Dragoi ◽  
Mostafa Eissa ◽  
Matthias Althoff
Keyword(s):  
Motion Planning ◽  
Open Source ◽  
Dynamic Behavior ◽  
Real World ◽  
Software Framework ◽  
Automated Vehicles ◽  
Lane Changing ◽  
Realistic Simulation ◽  
Lateral Dynamics ◽  
Planning Framework

Testing motion planning algorithms for automated vehicles in realistic simulation environments accelerates their development compared to performing real-world test drives only. In this work, we combine the open-source microscopic traffic simulator SUMO with our software framework CommonRoad to test motion planning of automated vehicles. Since SUMO is not originally designed for simulating automated vehicles, we present an inter- face for exchanging the trajectories of vehicles controlled by a motion planner and the trajectories of other traffic participants between SUMO and CommonRoad. Furthermore, we ensure realistic dynamic behavior of other traffic participants by extending the lane changing model in SUMO to implement more realistic lateral dynamics. We demonstrate our SUMO interface with a highway scenario.

scholarly journals Experimental Study under Real-World Conditions to Develop Fault Detection for Automated Vehicles

2012 ◽  
Vol 2 (5) ◽  
pp. 91-99 ◽  
Author(s):  
Naohisa Hashimoto ◽  
Umit Ozguner ◽  
Masashi Yokozuka ◽  
Shin Kato ◽  
Osamu Matsumoto ◽  
...  
Keyword(s):  
Experimental Study ◽  
Fault Detection ◽  
Real World ◽  
Automated Vehicles

Behavioral strategies of automated vehicles in critical right of way situations on intersections

2015 ◽  
Vol 63 (3) ◽  
Author(s):  
Alexander Stoff ◽  
Hermann Winner
Keyword(s):  
Collision Avoidance ◽  
Behavioral Strategies ◽  
Automated Vehicles ◽  
Right Of Way ◽  
Road Users ◽  
Safety Function ◽  
Potential Safety ◽  
Avoidance Strategies ◽  
The Right

AbstractThis paper analyzes and evaluates alternative options for action and earliest possible dates for intervention for an automated safety function to avoid or mitigate collisions in priority situations in which the right of way regulations are violated by the crossing road users. Based on a simulation of the collision avoidance strategies, the potential safety benefits could be predicted.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

2022 ◽  
pp. 036119812110702
Author(s):  
Shunchao Wang ◽  
Zhibin Li ◽  
Bingtong Wang ◽  
Jingfeng Ma ◽  
Jingcai Yu
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Motion Tracking ◽  
Collision Risk ◽  
Automated Vehicles ◽  
Vehicle Collision ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Velocity Obstacle ◽  
Vehicle Dynamic Model

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

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