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.
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.
Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.
The Single Cylinder Research Engine (SCRE) at the Institute of Internal Combustion Engines and Powertrain Systems is equipped with a variable valve train that allows to switch between regular intake valve lift and early intake valve closing (Miller). On the exhaust side, a secondary valve lift on each valve is possible with adjustable back pressure and thus the possibility of realising internal EGR. In combination with alternative fuels, even if they are Drop-In capable as HVO, properties differ and can influence the emission and efficiency behaviour. The investigations of this paper are focusing on regenerative Drop-In fuel (HVO), fossil fuel (B7), and an oxygenate (OME), that needs adaptions at the engine control unit, but offers further emission potential. By commissioning a 2-stage boost system, it is possible to fully equalize the air mass in Miller mode compared to the normal valve lift.
In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.
Engine and aftertreatment solutions have been identified to meet the upcoming ultra-low NOX regulations on heavy duty vehicles in the United States and Europe. These standards will require changes to current conventional aftertreatment systems for dealing with low exhaust temperature scenarios while increasing the useful life of the engine and aftertreatment system. Previous studies have shown feasibility of meeting the US EPA and California Air Resource Board (CARB) requirements. This work includes a 15L diesel engine equipped with cylinder deactivation (CDA) and an aftertreatment system that was fully DAAAC aged to 800,000 miles. The aftertreatment system includes an e-heater (electric heater), light-off Selective Catalytic Reduction (LO-SCR) followed by a primary aftertreatment system containing a DPF and SCR.
Previous studies have shown that dosing AdBlue into the exhaust system of diesel engines to reduce nitrogen oxides can lead to an increase in the number of particles (PN). In addition to the influencing factors of exhaust gas temperature, exhaust gas mass flow and dosing quantity, the dosed medium itself (AdBlue) is not considered as a possible influence due to its regulation in ISO standard 22241. However, as the standard specifies limit value ranges for the individual regulated properties and components for newly sold AdBlue, in reality there is still some margin in the composition. This paper investigates the particle number increase due to AdBlue dosing using several CPCs. The increase in PN is determined by measuring the number of particles after DPF and thus directly before dosing as well as tailpipe. Several AdBlue products from different sources and countries are measured and their composition is also analyzed with regard to the limit values regulated in the standard.
Ducted Fuel Injection (DFI) engines have emerged as a promising technology in the pursuit of a clean and efficient combustion process. This article aims at elucidating the effect of piston geometry on the engine performance and emissions of a metal DFI engine. Three different types of pistons were investigated and the main piston design features including the piston bowl diameter, piston bowl slope angle, duct angle and the injection nozzle position were examined. To achieve the target, computational fluid dynamics (CFD) simulations were conducted coupled to a reduced chemical kinetics mechanism. Extensive validations were performed against the measured data from a conventional diesel engine. To calibrate the soot model, genetic algorithm and machine learning methods were utilized. The simulation results highlight the pivotal role played by piston bowl diameter and fuel injection angle in controlling soot emissions of a DFI engine.
In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.
Aeroacoustics is important in the automotive industry, as it significantly influences driving comfort. Particularly in the case of battery electric vehicles (BEVs), the flow noise is already crucial at lower driving speeds, since these generate barely any drive noise and the masking effects produced by the engine are eliminated. Due to the increasing importance of drag minimization and elimination of the exhaust system, the underbody of BEVs is typically very streamlined and exhibits a low acoustic interference potential. However, even small geometric modifications to the vehicle can lead to changes in the flow around the vehicle and consequently to significant noise sources. Thus, significant flow resonances in the low frequency range below 30 Hz have been detected on certain vehicle configurations. Initial investigations have shown that the flow around the front wheel spoilers is relevant for the development of the flow phenomenon.
When travelling in an open-jet wind tunnel, the path of an acoustic wave is affected by the flow causing a shift of source positions in acoustical maps of phased arrays outside the flow. The well-known approach of Amiet attempts to correct for this effect by computing travel times between microphones and map points based on the assumption that the boundary layer of the flow, the so-called shear-layer, is infinitely thin and refracts the acoustical ray in a conceptually analogy to optics. However, in reality, the turbulent nature of both the not-so thin shear-layer and the acoustic emission process itself causes an additional smearing of sources in acoustic maps, which in turn causes deconvolution methods based on these maps - the most prominent example being CLEAN-SC - to produce certain ring effects, so-called halos, around sources.
By building on mature internal combustion engine (ICE) hardware combined with dedicated hydrogen (H2) technology, the H2-ICE has excellent potential to accelerate CO2 reduction. H2-ICE concepts can therefore contribute to realizing the climate targets in an acceptable timeframe. In the landscape of H2-ICE combustion concepts, High Pressure Direct Injection (HPDI™) is an attractive option considering its high thermal efficiency, wide load range and its applicability to on-road as well as off-road heavy-duty equipment. Still, H2-HPDI is characterized by diffusion combustion, giving rise to significant NOx emissions. In this paper, the potential of H2-HPDI toward compliance with future emissions legislation is explored on a 1.8L single-cylinder research engine. With tests on multiple load-speed points, Exhaust Gas Recirculation (EGR) was shown to be an effective measure for reducing engine-out NOx, although at the cost of a few efficiency points.
The current work presents the results of an investigation on the impact of renewable fuels on the combustion and emissions of a turbocharged compression-ignition internal combustion engine. An experimental study was undertaken and the engine settings were not modified to account for the fuel's chemical and physical properties, to analyze the performance of the fuel as a potential drop-in alternative fuel. Three fuels were tested: mineral diesel, a blend of it with waste cooking oil biodiesel and a hydrogenated diesel. The analysis of the emissions at engine exhaust highlights that hydrogenated fuel allows to reduce CO, total hydrocarbon emissions, particulate matter and NOx.
Ammonia has emerged as a promising carbon-free alternative fuel for internal combustion engines (ICE), particularly in large-bore engine applications. However, integrating ammonia into conventional engines presents challenges, prompting the exploration of innovative combustion strategies like dual-fuel combustion. Nitrous oxide (N2O) emissions have emerged as a significant obstacle to the widespread adoption of ammonia in ICE. Various studies suggest that combining exhaust gas recirculation (EGR) with adjustments in inlet temperature and diesel injection timing can effectively mitigate nitrogen oxides (NOx) emissions across diverse operating conditions in dual-fuel diesel engines.
Nowadays, the push for more ecological low-carbon propulsion systems is high in all mobility sectors, including the recreational or light-commercial boating, where propulsion is usually provided by internal combustion engines derived from road applications. In this work, the effects of replacing conventional fossil-derived B7 diesel with Hydrotreated Vegetable Oil (HVO) were experimentally investigated in a modern Medium-Duty Engine, using the advanced biofuel as drop-in and testing according to the ISO 8178 marine standard. The compounded results showed significant benefits in terms of NOx, Soot, mass fuel consumption and WTW CO2 thanks to the inner properties of the aromatic-free, hydrogen-rich renewable fuel, with no impact on the engine power and minimal deterioration of the volumetric fuel economy.
In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, lower carbon intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions.