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The nonlinear behavior of automotive powertrains is mainly due to the presence of backlash between engaging components. In particular, during tip-in or tip-out maneuvers, backlash allows the generation of impacts that negatively affect the vehicle NVH performance. Due to the faster response of electric motors with respect to conventional internal combustion engines, this problem is even more critical for electric vehicles. In order to employ numerical optimal control methods for backlash compensation, the system states have to be known. In this paper, an electric powertrain is modeled as a two-mass oscillator with lumped backlash. This model estimates the system states when in no-contact mode while a Kalman filter that relies only on commonly available speed measurements is active in the contact phase. The powertrain model is validated using experimental data collected during vehicle testing and the online estimated half-shaft torque is shown.

With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions.

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans’ driving behavior, in particular, their use of vision for ‘driving’ and map for ‘navigating’. The notion is that we humans use our vision to ‘drive’ and sometimes, we also use a map such as Google/Apple maps to find direction in order to ‘navigate’. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving.

The ported shroud casing treatment for turbocharger compressors offers a wider operating flow range, elevated boost pressures at low compressor mass flow rates, and reduced broadband whoosh noise in spark-ignition internal combustion engine applications. However, the casing treatment elevates tonal noise at the blade-pass frequency (BPF). Typical rotational speeds of compressors employed in practice push BPF noise to high frequencies, which then promote multi-dimensional acoustic wave propagation within the compressor ducting. As a result, in-duct acoustic measurements become sensitive to the angular location of pressure transducers on the duct wall. The present work utilizes a steady-flow turbocharger gas stand featuring a unique rotating compressor inlet duct to quantify the variation of noise measured around the duct at different angular positions.

Past studies have found that a pressure based injury risk function was the best predictor of liver injuries due to blunt impacts. In an effort to expand upon these findings, this study investigated the biomechanical responses of the abdomen of post mortem human surrogates (PMHS) to high-speed seatbelt loading and developed external response targets in conjunction with proposing an abdominal injury criterion. A total of seven unembalmed PMHS, with an average mass and stature of 71 kg and 174 cm respectively were subjected to belt loading using a seatbelt pull mechanism, with the PMHS seated upright in a free-back configuration. A pneumatic piston pulled a seatbelt into the abdomen at the level of the umbilicus with a nominal peak penetration speed of 4.0 m/s. Pressure transducers were placed in the re-pressurized abdominal vasculature, including the inferior vena cava (IVC) and abdominal aorta, to measure internal pressure variation during the event.

Heavy trucks are involved in many accidents every year and Electronic Stability Control (ESC) is viewed as a means to help mitigate this problem. ESC systems are designed to reduce the incidence of single vehicle loss of control, which might lead to rollover or jackknife. As the working details and control strategies of commercially available ESC systems are proprietary, a generic model of an ESC system that mimics the basic logical functionality of commercial systems was developed. This paper deals with the study of the working of a commercial ESC system equipped on an actual tractor trailer vehicle. The particular ESC system found on the test vehicle contained both roll stability control (RSC) and yaw stability control (YSC) features. This work focused on the development of a reliable RSC software model, and the integration of it into a full vehicle simulation (TruckSim) of a heavy truck.

This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically.