Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Collision avoidance systems like emergency braking assist systems have demonstrated their effectiveness in increasing the safety of vehicle passengers in various studies. To further increase the effectiveness of collision avoidance systems, the exploitation of the lateral free space by evasive maneuvers is being investigated in this book. This work focuses on methods for integrated trajectory planning and vehicle dynamics control in collision avoidance scenarios by combined evasion and braking.

Fahrdynamikregelung --- model predictive control --- Collision Avoidance System --- Modelprädiktive Regelung --- Kollisionsvermeidung --- Trajectory Planning --- Driver Assistance System --- Fahrassistenzsystem --- Trajektorienplanung --- Vehicle Dynamics Control

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Traffic safety. --- Automobile driving --- Highway safety --- Road safety --- Traffic accidents --- Public safety --- Traffic engineering --- Transportation, Automotive --- Automobiles --- Safety measures --- Prevention --- Collision avoidance systems

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations.

Technology: general issues --- History of engineering & technology --- physical human-robot interaction --- game theory --- adaptive optimal control --- robot control --- tandem force sensor --- traction force sensor --- human–robot interaction --- contact task --- imitation learning --- safe physical human–robot collaboration --- collision detection --- human action recognition --- artificial intelligence --- industrial automation --- reinforcement learning --- social robotics --- human-robot interaction --- reward design --- physical embodiment --- human robot collaboration --- human robot interaction --- path planning --- bidirectional awareness --- haptic feedback device --- human machine interface --- collision identification --- collaborative robot --- deep learning --- uncertainty estimation --- knowledge distillation --- human–robot collaboration --- speed and separation monitoring --- human–machine differentiation --- thermal cameras --- protective separation distance --- collaborative robots --- motion planning --- human motion prediction --- human-following robots --- teleoperation --- high-speed image processing --- machine learning --- finger position recognition --- grasp type estimation --- human-robot collaboration --- human-centered robotics --- task planning --- n/a --- safe physical human-robot collaboration --- human-machine differentiation

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs.

TROOP --- truck platooning --- path planning --- kalman filter --- V2V communication --- string stability --- off-tracking --- articulated cargo trucks --- kabsch algorithm --- potential field --- sigmoid curve --- autonomous vehicles --- connected and autonomous vehicles --- artificial neural networks --- end-to-end learning --- multi-task learning --- urban vehicle platooning --- simulation --- attention --- executive control --- simulated driving --- task-cuing experiment --- electroencephalogram --- fronto-parietal network --- object vehicle estimation --- radar accuracy --- data-driven --- radar latency --- weighted interpolation --- autonomous vehicle --- urban platooning --- vehicle-to-vehicle communication --- in-vehicle network --- analytic hierarchy architecture --- traffic scenes --- object detection --- multi-scale channel attention --- attention feature fusion --- collision warning system --- ultra-wideband --- dead reckoning --- time to collision --- vehicle dynamic parameters --- Unscented Kalman Filter --- multiple-model --- electric vehicle --- unified chassis control --- unsprung mass --- autonomous driving --- trajectory tracking --- real-time control --- model predictive control --- tyre blow-out --- yaw stability --- roll stability --- vehicle dynamics model --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments.

Particle & high-energy physics --- Mensuration & systems of measurement --- Pattern recognition --- Mathematical physics --- Particle Acceleration and Detection, Beam Physics --- Measurement Science and Instrumentation --- Pattern Recognition --- Numerical and Computational Physics, Simulation --- Accelerator Physics --- Automated Pattern Recognition --- Theoretical, Mathematical and Computational Physics --- Event reconstruction --- Tracking detectors in High Energy Physics --- Vertex reconstruction --- Clustering algorithms --- Experimental High-Energy Physics --- LHC --- Calolimator for pattern recognition --- Vertex of particle collision --- Triggering event and data analysis --- Open access --- Scientific standards, measurement etc