Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）；AUDI AG（奥迪股份公司）
Eduardo Sánchez Morales, Richard Membarth, Andreas Gaull, Philipp Slusallek, Tobias Dirndorfer, Alexander Kammenhuber, Christoph Lauer, Michael Botsch
Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2.
Multi-level decision framework collision avoidance algorithm in emergency scenarios
State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车仿真与控制国家重点实验室）；School of Engineering, University of Birmingham（伯明翰大学工程学院）；School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
Efficient COLREGs-Compliant Collision Avoidance using Turning Circle-based Control Barrier Function
Mobile Robotics and Intelligence Laboratory, Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)（移动机器人与智能实验室，机械工程系，韩国科学技术院）
ARCAS: An Augmented Reality Collision Avoidance System with SLAM-Based Tracking for Enhancing VRU Safety
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；School of Architecture, The University of Texas at Austin（德克萨斯大学奥斯汀分校建筑学院）
Online Action-Stacking Improves Reinforcement Learning Performance for Air Traffic Control
Project Bluebird, NATS（Project Bluebird，NATS）；Department of Computer Science, University of Exeter（计算机科学系，埃克塞特大学）；Project Bluebird, The Alan Turing Institute（Project Bluebird，艾伦·图灵研究所）；The Alan Turing Institute and the University of Exeter（艾伦·图灵研究所和埃克塞特大学）
