Search Results

The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.  

The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

Due to its physical and chemical properties, hydrogen is an attractive fuel for internal combustion engines, providing grounds for studies on hydrogen engines. It is common practice to use a mathematical model for basic engine design and an essential part of this is the simulation of the combustion cycle, which is the subject of the work presented here. One of the most widely used models for describing combustion in gasoline and diesel engines is the Wiebe model. However, for cases of hydrogen combustion in DI engines, which are characterized by mixture stratification and in some cases significant incomplete combustion, practically no data can be found in the literature on the application of the Wiebe model. Based on Wiebe's formulas, a mathematical model of hydrogen combustion has been developed. The model allows making computations for both DI and PFI hydrogen engines. The parameters of the Wiebe model were assessed for three different engines in a total of 26 operating modes.

Multiple three-phase machines have become popular in recent due to their reliability, especially in the ship and airplane propulsions. These systems benefit greatly from the robustness and efficiency provided by such machines. However, a notable challenge presented by these machines is the growth of harmonics with an increase in the number of phases, affecting control precision and inducing torque oscillations. The phase shift angles between winding sets are one of the most important causes of harmonics in the stator currents and machine torque. Traditional approaches in the study of triple-three-phase or nine-phase machines mostly focus on specific phase shift, lacking a comprehensive analysis across a range of phase shifts. This paper discusses the current and torque harmonics of triple-three-phase permanent magnet synchronous machines (PMSM) with different phase shifts. It aims to analyze and compare the impacts of different phase shifts on harmonic levels.

In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm based on GNSS, vehicle odometry, IMU and LiDAR data. The algorithm is adapted to the use-case by adding a velocity factor and altitude data from a Digital Terrain model. Based on the map a state estimator is proposed, which combines high-frequency LiDAR odometry based on FAST-LIO with low-frequency absolute multiscale ICP-based LiDAR position estimation.