Organizational culture in the United States automotive industry

Organizational culture is a product of both internal and external factors. Leaders sometimes attempt to change culture using a variety of mechanisms, while the effects of external environment simultaneously exert influence. The external environment effects can be especially pronounced when comparing a traditional, Midwestern automotive firm (General Motors) with a Silicon Valley automotive startup (Tesla). A study was conducted to compare the social system of these two automakers and to identify some of the factors, both internal and external, shaping their culture. Data was collected on the employment history of engineers and managers using the social networking platform LinkedIn.com. Similarly, publicly-posted employee reviews were collected from the website Glassdoor.com and analyzed using a novel method for classifying and analyzing openorm written survey responses. Together, these records paint a picture of the employee perceptions of culture for both companies, the breadth of external experience represented in their workforces, and the tendency to fill management positions from internal candidates. The results suggest that external environment has broad effects on workforce experience, thereby creating certain cultural attributes such as loyalty or a drive to innovate. The results also suggest that promoting internal candidates more often does not necessarily lead to employee perception of better career opportunities. Taken together, the results reinforce but expand traditional views of organizational culture and call for further study on the matter across more industry groups

A Petroleum Energy, Greenhouse Gas, and Economic Life Cycle Analysis of Several Automotive Fuel Options

A vehicle fuel’s life does not begin when that fuel is pumped into the tank or the battery is charged. Each kilowatt-hour of fuel that is used has a history traceable back to its original feedstock, be it crude oil, corn, solar energy, or others. In this thesis, a life cycle analysis is performed on E10, E85, B20, hydrogen, and electricity, with the well-to-pump fossil fuel energy use and greenhouse gas emissions compared. Results are presented in the form of either energy or mass per kilowatt of fuel at the plug or at the pump. An analysis of the economic viability of each fuel to the consumer is also demonstrated. E85 is found to have the best well-to-pump fossil fuel energy use at 722 Wh/kWh, while hydrogen demonstrates the best well-to-wheel greenhouse gas emissions with 123 g/km (CO2 equivalent) and electricity produces the lowest vehicle lifetime operating cost of $0.241/mile

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation

Predicting the Influence of Rain on LIDAR in ADAS

While it is well known that rain may influence the performance of automotive LIDAR sensors commonly used in ADAS applications, there is a lack of quantitative analysis of this effect. In particular, there is very little published work on physically-based simulation of the influence of rain on terrestrial LIDAR performance. Additionally, there have been few quantitative studies on how rain-rate influences ADAS performance. In this work, we develop a mathematical model for the performance degradation of LIDAR as a function of rain-rate and incorporate this model into a simulation of an obstacle-detection system to show how it can be used to quantitatively predict the influence of rain on ADAS that use LIDAR

Rollover-Free Path Planning for Off-Road Autonomous Driving

Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving

An Experiment-Based Methodology for Evaluating the Impacts of Full Bandwidth Load on the Hybrid Energy Storage System for Electrified Vehicles

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology

Investigating the Relationship Between In-Process Quality Metrics and Mechanical Response in the L-PBF Process

Laser powder bed fusion (L-PBF) additive manufacturing is a process that utilizes a high- powered laser to build near net-shaped parts in a layer-by-layer fashion using metal powder as the feedstock material. Traditionally, the analysis of L-PBF produced parts has relied solely on post- build characterization to understand the relationship between the printing process and the final mechanical properties. Recent developments of in-process quality assurance systems, such as Sigma Additive Solutions’ PrintRite3D, can measure in-process thermal signatures and melt pool disturbances in real-time. This research aims to examine the relationship between process parameters (e.g., scan strategy, scanning speed, and layer thickness) and in-process quality metrics (IPQMs) captured by the PrintRite3D system on a Renishaw AM400. The mechanical response of multiple part geometries (NIST residual stress bridges, single-arched bridges) and build materials (Ti6Al4V) includes residual stress deflection and hardness; the results are compared with the IPQMs.Mechanical Engineerin

Effects of Process Interruption During Laser Powder Bed Fusion on Microstructural and Mechanical Properties of Fabricated Parts

Despite appropriate planning, various incidents can stop the additive manufacturing (AM) process of metals and cause build interruption, such as power outage, lack of powder feedstock, and/or shielding gas to mention a few. Due to the layer-by-layer nature of fabrication, an interruption to the AM process can be resumed from the location where the stoppage occurred. However, build interruption may adversely affect the structural integrity of the fabricated parts, by causing localized failure near the interruption location. This study aims to investigate the influence of build interruption during the laser powder bed fusion (LPBF) process on the microstructural and mechanical properties of Ti-6Al-4V and Al-Si-10Mg specimens. For the Ti-6Al-4V specimens, results indicate that tensile failures near the interruption location are most likely to happen for non-heat-treated specimens in the as-built surface condition. Whereas for the Al-Si- 10Mg specimens, the failure location is more influenced by the prolonged stoppage and air exposure.Mechanical Engineerin