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The Road to the Top is Not on the Map: Conversations with Top Women of the Automotive Industry

Terry Barclay
Center For Automotive Research-Carla Bailo
  • Book
  • R-491
To be published on 2019-10-01 by SAE International in United States

Carla Bailo, CEO of the Center for Automotive Research, and Terry Barclay, CEO of Inforum, bring together over 30 of the most influential women in the automotive industry to share their insight and advice. From suppliers to OEMs, they hail from every corner of the industry.

An Interactive Vehicle Recommender System Based on Decision Trees and Multi-Armed Bandits

Carnegie Mellon University-Tong Yu, Ole Mengshoel
Ford Motor Company-Dominique Meroux, Zhen Jiang
  • Technical Paper
  • 2019-01-1079
To be published on 2019-04-02 by SAE International in United States
Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study how to make recommendations for vehicle purchases. This can effectively reduce the human labor in the traditional setting, where customers get recommended vehicles through conversations with the salesmen in dealerships. Comparing a vehicle recommender system to one in other application domains (movies, music, etc.), we identify two major challenges. First, customers usually only purchase a limited number of vehicles, compared to the number of movies or songs. Thus, it is difficult to obtain rich information about a user's purchase history. Second, the content information obtained about the users (demographic, vehicle preference, etc.) is also very limited during their short stay in the dealership. To address these two challenges, we propose an interactive vehicle recommender system based on the methods of decision tree classification and multi-armed bandit. The decision tree effectively selects important questions for the user and understands the user's preference. With these preference as prior information, the multi-armed bandit algorithm…

Driver’s response prediction using Naturalistic Data Set

Ohio State Univ-Dennis Guenther
Ohio State University-Venkata Raghava Ravi Lanka
  • Technical Paper
  • 2019-01-0128
To be published on 2019-04-02 by SAE International in United States
Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle's decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle's behavior. This predicted response of the vehicle can be used in validating vehicle's safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver's response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models. Various popular Machine Learning Algorithms were used for classifying and predicting driver's response, such as Extremely Randomized Trees and Gaussian Mixture Model based Hidden Markov Model, which are widely used in multiple domains. For classifying driver's response, longitudinal acceleration vs lateral acceleration plot (Ax-Ay plot) was divided into nine different classes and selected Machine Learning models were trained…

Research on The Model of Safety Boundary Condition Based on Vehicle Intersection Conflict and Collision

Tongji University-Biao Wu, Xichan Zhu, Maozhu Liao, Rui Liu
  • Technical Paper
  • 2019-01-0132
To be published on 2019-04-02 by SAE International in United States
Because of the high frequency and serious consequences of traffic accidents in the intersection area, it is of great significance to study the vehicle conflict and collision scenarios of the intersection area. Due to few actual crash accidents occurring in naturalistic driving studies data or field operational tests data, the data of traffic accident database should be also used to analyze for the intersection conflict and collision study. According to the China Field Operation Test (China-FOT) database and the China in Depth Accident Study (CIDAS) database, the distribution feature of the respective intersection scenario type is obtained from the data analysis. Based on the intersection scenario type, two characters of intersection conflict and collision, that the environmental character and the vehicle dynamic character, are used to analyze for the integration process of intersection conflict and collision. The environmental character contains environment type, weather and time parameters, which has a strong influence on the collision accidents. During the environmental parameters analysis, the distribution model of environmental character is obtained. The vehicle dynamic character includes two vehicle…

Prediction of Human Actions in Assembly Process by a Spatial-Temporal End-to-End Learning Model

Clemson Univ-Zhujun Zhang, Weitian Wang, Yi Chen, Yunyi Jia
Harbin Institute of Technology-Zhujun Zhang, Gaoliang Peng
  • Technical Paper
  • 2019-01-0509
To be published on 2019-04-02 by SAE International in United States
It’s important to predict the future actions of human in the industry assembly process. Foreseeing future actions before they have happened is an essential part for flexible human-robot collaboration and crucial safety issues. Vision-based human actions prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations between each time step for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with the traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in past human motions and construct the future action based on limited accessible human demonstrations. In this paper, we apply an autoencoder based deep learning framework for human actions construction, merging into the RNN pipeline for human future actions prediction. This contrasts with traditional approaches which use hand-crafted features and different domain output. The…

Automated Vehicle Disengagement Reaction Time Compared to Human Reaction Times in Both Automobile and Motorcycle Operation

Dynamic Analysis Group LLC-Jeffrey T. Dinges, Nicholas J. Durisek
  • Technical Paper
  • 2019-01-1010
To be published on 2019-04-02 by SAE International in United States
Autonomous Vehicle Disengagement Reports have been published by the California Department of Motor Vehicles since 2015. Some of the autonomous control system manufacturers and vehicle manufacturers provide information that includes the time that it takes for a human driver to take manual control of the vehicle when reporting on vehicle disengagements. This study compares the reported autonomous vehicle operation disengagement reaction time to field literature in testing and experimentation on human reaction times for both automobile and motorcycle operation. The study will address the types of autonomous vehicle disengagements that occurred during the collection along with the understood conditions that surround the disengagement. It will also address how autonomous vehicle disengagements and general human perception and reaction performance influences autonomous vehicle operation.

Research on the adaptability of vehicles with different technical types to the China 6 regulation standard

CATARC-Feng Yan
  • Technical Paper
  • 2019-01-0759
To be published on 2019-04-02 by SAE International in United States
A test fleet formed of 47 gasoline vehicles with 6 different technical types was organized, and the adaptability of these vehicles under China 6 standard was studied. By analyzing the performance of the vehicles tested in laboratory and in real-world, the technical types that could simultaneously reconcile emission and fuel consumption performance were proposed. The results show that all vehicles are well performed under laboratory conditions, but in real-world conditions, 50% direct injection vehicles without GPF would fail the RDE test. Better performance occurred in vehicles equipped with GPF, all of which passed the RDE tests even if the conformity factor decreased from 2.1 to 1. In addition, it is found that the fuel consumption of the DI vehicle were lower than that of the PFI vehicle, and the DI vehicle with GPF shows better performance on the balance of emission and fuel consumption, which have more potentials to deal with the increasing tightened emission and fuel consumption standards at the same time.

Accuracy and sensitivity of yaw speed analysis to available data

MEA Forensic Engineers & Scientists-Bradley E. Heinrichs, Janice Lee, Cole Young
  • Technical Paper
  • 2019-01-0417
To be published on 2019-04-02 by SAE International in United States
Accident reconstructions rarely have complete data with which to determine vehicle speed, and so must bracket the true value with a range. Previous work has shown the effect of friction uncertainty in determining speed from tire marks left by a vehicle in yaw. The goal of the current study was to assess improvements in the accuracy of vehicle speed estimated from yaw marks using progressively more scene and vehicle information. Data for this analysis came from staged S-turn maneuvers that in some cases led to rollover of the SUV test vehicles. Initial speeds were first calculated using the critical curve speed (CCS) formula on the yaw marks from the first portion of the S-maneuver. Then computer simulations were performed with progressively more input data: i) the complete tire marks from the whole S-maneuver, ii) measured vehicle mass, iii) measured suspension stiffness and damping, and iv) measured steering history. Simulations based on the complete tire marks improved the average error compared with the CCS equation. Adding the remaining input data to the simulations did not further…

Vehicle cold start Mode fuel economy simulation model making methodology

Maruti Suzuki India Ltd-Parikshit Mehra, Amit Gautam
Maruti Suzuki India, Ltd.-Bhoopendra Singh
  • Technical Paper
  • 2019-01-0898
To be published on 2019-04-02 by SAE International in United States
The air pollution and global warming has become a major problem to the society. To counter this worldwide emission norms have become more stringent in recent times and shall continue to get further stringent in the next decade. From OEMs perspective with increased complexity, it has become a necessity to use simulation methods along with model based systems approach to deal with system level complexities and reduce model development time and cost to deal with the various regulatory requirements and customer needs. The simulation models must have good correlation with the actual test results and at the same time should be less complex, fast, and integrable with other vehicle function modelling. As the vehicle fuel economy is declared in cold start condition, the fuel economy simulation model of vehicle in cold start condition is required. The present paper describes a methodology to simulate the cold start fuel economy. The simulation methodology includes the engine heat balance equation, heat conduction through cylinder walls and heat convected by air. Based on the heat transferred and heat absorbed…

Trust-based Control and Scheduling for UGV Platoon under Cyber Attacks

Clemson Univ-Yue Wang
Clemson Univ.-John R. Wagner
  • Technical Paper
  • 2019-01-1077
To be published on 2019-04-02 by SAE International in United States
Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and on-board intelligence can help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs which may reduce operational performance. Ideally, an automated decision-making system can be designed that empowers collaboration between the human operator and UGVs. In this paper, a connected UGV platoon, vehicle-to-vehicle (V2V) and vehicle-to-cloud (V2C), experiences a cyber-attack which may disrupt the mission. In the proposed framework, an observer-based resilient control strategy is designed to mitigate the effects of V2V cyber-attacks. In addition, each UGV also generates an internal, or self, and external, or surrounding neighbors, evaluation based on the platoon’s performance metrics. A cloud-based trust-based information management system collects these evaluations and detects abnormal UGV platoon behavior. In dealing with inaccurate information due to a V2C cyber-attack, a RoboTrust algorithm analyzes vehicle trustworthiness and eliminates information with low credit. Finally, a human operator scheduling algorithm is proposed when the number of abnormal UGVs exceeds the limit of…