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Technical Paper

Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

2022-03-29
2022-01-0899
As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to improve the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness, however, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific applications. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven approach of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.
Technical Paper

Characterisation of the Tyre Spray Ejected Downstream of a Bluff Automotive Body

2022-03-29
2022-01-0893
A substantial obstacle to the widespread adoption of autonomous vehicles is the perception of safety and reliability. Safe operation relies on an increasing array of sensors, the performance of which may be impaired by both airborne spray and direct obstruction. This necessitates increased consideration of both tyre spray and surface contamination throughout the automotive design process. Existing work has shown spray entrainment to be an unsteady problem, but typical experimental investigations of front-end contamination and wiper performance have utilised fixed spray grids. Such methods do not replicate the turbulence generated by preceding vehicles and may, therefore, be a poor representation of third-party contaminant sources. This paper presents an investigation into the effect of rear-end geometry changes on the spray ejected downstream of a bluff automotive body (the Windsor Model).
Technical Paper

Development of a Prediction Model for Tire Tread Pattern Noise based on Convolutional Neural Network with RMSProp Algorithm.

2022-03-29
2022-01-0884
It has changed from an internal combustion engine to an electric vehicle(EV) so the exterior noise of the engine and exhaust system is removed and the tire pattern noise becomes the main noise source contributor for EV traveling at speeds above 50 kph and 80 kph. It is very useful to predict tire pattern noise performance in the early tire design stage. An artificial neural network model was recently used for the prediction of tire pattern noise. The ANN used the supervised training method, in which features are extracted by applying Gaussian curve fitting to the tread profile spectra of tire patterns; these are then used as the inputs of the ANN. Nevertheless, this method requires the laser scanning of the patterns of real tires, which are non-existent as they are still in the early design stage.
Technical Paper

Optimized Variable Gear Ratio Steering System with Reduced EPAS Motor Size achieving Performance Targets

2022-03-29
2022-01-0874
In electric power assisted steering system (EPAS), the steering assistance torque is provided by the electric motor. The motor rating is decided based on rack force requirement which depends on the vehicle weight, steering gear ratio, wheel angles & turning circle diameter etc. The load on the EPAS motor varies with respect to the steered angles of the road wheels. The motor experiences higher load towards the road wheel lock position. Most of the steering systems used on passenger cars has rack and pinion gear with constant gear ratio (C-factor). The constant gear ratio is decided to create right balance between vehicle handling behavior and steering effort. The constant gear ratio exerts higher steering load which the EPAS motor is required to support up to road wheel lock angles and hence EPAS motor size increases. This paper presents variable gear ratio (VGR) steering system in which gear ratio varies from center towards end lock stroke of rack & pinion.
Technical Paper

Tire Track Identification: A Method for Drivable Region Detection in Conditions of Snow-Occluded Lane Lines

2022-03-29
2022-01-0083
Today’s advanced driver-assistance systems (ADAS) commonly utilize the camera sensor to provide the benefit of increased safety to the driver. This is done by using computer vision to detect two key environmental features: lane lines and objects (e.g., vehicles, pedestrians, bicycles). Lane detections are vital for safety features such as lane departure warnings, lane-keeping assist systems, and lane-centering systems. However, when subjected to adverse weather conditions, either heavy perception of rain or snow or occlusion of lane lines due to rain or snow, the lane detection algorithms are no longer capable of detecting the lane lines. Hence, the ADAS feature is no longer providing the benefit of increased safety to the driver. The performance of one of the leading computer vision system providers was tested in conditions of variable snow coverage on the road, causing occluded lane lines, using data collected in the 2020-2021 winter in Kalamazoo, Michigan.
Technical Paper

Lane Change Decision Algorithm Based on Deep Q Network for Autonomous Vehicles

2022-03-29
2022-01-0084
For high levels autonomous driving functions, the decision layer often takes on more responsibility due to the requirement of facing more diverse and even rare conditions. It is very difficult to accurately find a safe and efficient lane change timing when autonomous vehicles encounter complex traffic flow and need to change lanes. The traditional method based on rules and experiences has the limitation that it is difficult to be taken into account all possible conditions. Therefore, this paper designs a lane-changing decision algorithm based on data-driven and machine learning, and uses the DQN (Deep Q Network) algorithm in Reinforcement Learning to determine the appropriate lane-changing timing and target lane. Firstly, the scene characteristics of the highway are analyzed, the input and output of the decision-making model are designated and the data from the perception layer are processed.
Technical Paper

Higher Accuracy and Lower Computational Perception Environment Based Upon a Real-time Dynamic Region of Interest

2022-03-29
2022-01-0078
Robust sensor fusion is a key technology for enabling the safe operation of automated vehicles. Sensor fusion typically utilizes inputs of cameras, radars, lidar, inertial measurement unit, and global navigation satellite systems, process them, and then output object detection or positioning data. This paper will focus on sensor fusion between the camera, radar, and vehicle wheel speed sensors which is a critical need for near-term realization of sensor fusion benefits. The camera is an off-the-shelf computer vision product from MobilEye and the radar is a Delphi/Aptive electronically scanning radar (ESR) both of which are connected to a drive-by-wire capable vehicle platform. We utilize the MobilEye and wheel speed sensors to create a dynamic region of interest (DROI) of the drivable region that changes as the vehicle moves through the environment.
Technical Paper

Design and Hardware in the Loop testing of AEB controllers

2022-03-29
2022-01-0099
Current ADAS systems can improve vehicle safety directly influencing its dynamics, reducing the impact of human error while driving. These functionalities have a high impact on the complexity of each unit installed on the car, potentially increasing the development time. In this work, a Hardware in the Loop testing methodology for Autonomous Emergency Braking system is presented, aiming to enable a faster system development process. A commercial production brake by wire unit has been installed on a real-time driving simulator. The AEB functionality of the unit is activable in real-time during the simulation, by the means of a customizable control strategy. Three different AEB controllers have been implemented: the first one reproduces the unit stock functionality, while the two remaining compute the requested deceleration using a PID and a Fuzzy control strategy.
Technical Paper

Development of an Integrated Co-Simulation Environment to Model and Test Advanced DAT Features Utilizing MIL and HIL

2022-03-29
2022-01-0101
The widespread deployment of Connected and Automated Vehicles (CAVs) in the future will bring major changes in the automotive industry and the vehicle features it will offer. Currently, technology and infrastructure are not ready to test and develop CAV features fully in real traffic. Simulators are becoming popular to develop CAVs and assisted automated driving features for all levels of automation to overcome the infrastructural needs of the automotive industry. Simulators allow researchers to design CAV algorithms safely, quickly, and efficiently, and test these algorithms for various metrics. A co-simulation environment, where a vehicle simulator like CarSim and a traffic simulator like Simulation of Urban Mobility (SUMO) feed into each other, is an invaluable tool, allowing CAV features to be tested in a realistic traffic environment. This paper presents a co-simulation environment, where the vehicle simulator CarSim and the traffic simulator SUMO share data.
Technical Paper

A humanized vehicle speed control to improve the acceptance of automated longitudinal control

2022-03-29
2022-01-0095
Vehicle speed controls, as adaptive cruise control and related automated evolutions, are control systems able to follow a desired vehicle reference speed that is set by the driver and fused with information as road signs, SD maps etc.. Current normal production systems don’t distinguish among the vehicle users, only some carmakers are doing first steps towards the introduction of learning from driver to adapt the traditional control. In our work, we follow up this content with a humanized speed control, based on learning of driver longitudinal behavior. This method is able to combine machine learning algorithms, vehicle positioning and recurrent trips into existing automated longitudinal control systems. Proposed algorithm can reduce the interactions between drivers and automated systems by improving the acceptance of automated longitudinal control. Furthermore, proposed integration works mainly on speed reference that dramatically simplifies the customization of the system.
Technical Paper

Development of Argonne VIL workflow for CAVs to demonstrate the energy-efficient automated driving controls in real vehicles

2022-03-29
2022-01-0420
Argonne National Laboratory (ANL) has developed automated driving control strategies that aim at maximizing energy-efficiency. Up to 22% energy savings were demonstrated [1] in a large-scale study performed in RoadRunner, a tool for the energy-centric modeling of connected and automated vehicles (CAVs). This paper describes how these controls were implemented and tested in real vehicles on dynamometer setting, ANL vehicle in the loop (VIL) workflow, to demonstrate both the efficacy of the controls themselves, but also to validate the RoadRunner models. The main concept of the ANL VIL workflow is to build a digital twin and connect to a real vehicle on dynamometer as if it can drive on the real road. The ANL VIL workflow mainly consists of three steps, integration, preparation, and experimentation. A digital twin of the vehicle and the testing environment was built by the help of RoadRunner for the integration.
Technical Paper

A three-dimensional flame reconstruction method for SI combustion based on two-dimensional images and geometry model

2022-03-29
2022-01-0431
An attempt has been made to develop a feasible method to reconstruct the three-dimensional flame surface of SI combustion. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. High speed flame image acquisition were conducted from two different optical windows. The flame front was re-constructed based on two-dimensional images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision analysis. The flame irregularity and anisotropy were also taken into consideration. Two different principles were used to build the model, which were called the ellipse hypothesis modeling and a deep learning modeling. The reconstruction results show that part of the wrinkled and folded structure of the flame front can be revealed based on the bottom view images.
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