A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaxin Li ◽  
Weiwen Deng ◽  
Bohua Sun ◽  
Jinsong Wang ◽  
Jian Zhao ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Threat Assessment ◽  
Assessment Strategy

A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

2018 ◽  
Author(s):  
Yaxin Li ◽  
Weiwen Deng ◽  
Bohua Sun ◽  
Jinsong Wang ◽  
Jian Zhao
Keyword(s):  
Collision Avoidance ◽  
Threat Assessment ◽  
Assessment Strategy

A review on threat assessment, path planning and path tracking strategies for collision avoidance systems of autonomous vehicles

2018 ◽  
Vol 14 (2) ◽  
pp. 134
Author(s):  
Pongsathorn Raksincharoensak ◽  
Mohd Azizi Abdul Rahman ◽  
Umar Zakir Abdul Hamid ◽  
Yuichi Saito ◽  
Hairi Zamzuri
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Threat Assessment ◽  
Path Tracking

scholarly journals Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

SIMULATION ◽  
2021 ◽  
pp. 003754972110031
Author(s):  
Omar Hesham ◽  
Gabriel Wainer
Keyword(s):  
Collision Avoidance ◽  
State Of The Art ◽  
Threat Assessment ◽  
Personal Space ◽  
Particle Dynamics ◽  
Emergent Behavior ◽  
Trajectory Data ◽  
Agent Based ◽  
Dense Crowds ◽  
Dense Crowd

Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with typically sparse pedestrian scenarios. Centroidal particle dynamics (CPD) is a method we defined that explicitly models the compressible personal space area surrounding each entity to inform its local pathing and collision-avoidance decisions. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level with higher congruence to real trajectory data and with more visually convincing collision-avoidance paths than the existing state of the art. We present advanced models in which we consider distraction of the pedestrians in the crowd, flocking behavior, interaction with vehicles (ambulances, police) and other advanced models that show that emergent behavior in the simulated crowds is similar to the behavior observed in reality. We discuss how to increase confidence in CPD, potentially making it also suitable for use in safety-critical applications, including urban design, evacuation analysis, and crowd-safety planning.

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