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Viewing 1 to 30 of 841
2017-03-28
Technical Paper
2017-01-0101
Alexandru Gurghian, Francois Charette
This paper presents the implementation of a 1/10th scale track with multiple vehicles that serves as a framework used for introducing new employees, interns or groups of student to autonomous vehicle and ADAS technologies. The framework allows new generations of potential engineers to experience software development at the intersection between computer science and engineering. Additionally, the framework can serve as a gentle and exciting introduction to automotive software development. The proposed system is based on an off the shelf 1/10th scale remote controlled car and is equipped with an Intel NUC, a full-fledged computer powered by an Intel i7 processor, providing sufficient compute power for computationally expensive perception and control algorithm. Steering and throttle actuators are accessible through a microcontroller connected to the Intel NUC via USB, which also acts as a safety controller that allows to override control signals with a remote control.
2017-03-28
Technical Paper
2017-01-0024
Yuto Imanishi, Naoyuki Tashiro, Yoichi Iihoshi, Takashi Okada
In recent years, improvement of in-use fuel economy is required with tightening of exhaust emission regulation. We assume that one of the most effective solutions is ACC (Adaptive Cruise Control), which can control a powertrain in detail more than a driver. We have been developing a fuel saving ADAS application named “Sailing-ACC”. Sailing ACC system uses sailing stop technology which stops the engine fuel injection and disengage clutch when the car do not need acceleration torque. This system has a potential to greatly improve fuel efficiency. In this paper, we present a predictive powertrain state switching algorithm using external information (route information, preceding vehicle information). This algorithm calculates appropriate switching timing to sailing stop mode and acceleration mode to avoid braking loss. To design switching algorithm, we clarify the characteristics of driving technique called “pulse and glide”.
2017-03-28
Technical Paper
2017-01-0031
Mohamed Benmimoun
In the last years various advanced driver assistance systems (ADAS) have been introduced in the market. More highly advanced functions up to automated driving functions are currently under research. By means of these functions partly automated driving in specific situations is already or will be soon realized, e.g. traffic jam assist. Besides the technical challenges to develop such automated driving functions for complex situations, e.g. construction or intersection areas, new approaches for the evaluation of these functions under different driving conditions are necessary, in order to assess the benefits and identify potential weaknesses. Classical approaches for evaluation and market sign off will require an extensive testing, which results in high costs and time demands. Therefore the classical approaches are hardly feasible taking into account higher levels of support and automation. Today the final sign-off requires a high amount of real world tests.
2017-03-28
Technical Paper
2017-01-0038
Corwin Stout, Milos Milacic, Fazal Syed, Ming Kuang
In recent years, we have witnessed increased discrepancy between fuel economy numbers reported in accordance with EPA testing procedures and real world fuel economy reported by drivers. The debates range from needs for new testing procedures to the fact that driver complaints create one-sided distribution; drivers that get better fuel economy do not complain about the fuel economy, but only the ones whose fuel economy falls short of expectations. In this paper, we demonstrate fuel economy improvements that can be obtained if the driver is properly sophisticated in the skill of driving. Implementation of SmartGauge with EcoGuide into the Ford C-MAX Hybrid in 2013 helped drivers improve their fuel economy on hybrid vehicles. Further development of this idea led to the EcoCoach that would be implemented into all future Ford vehicles.
2017-03-28
Technical Paper
2017-01-0070
Longxiang Guo, Sagar Manglani, Xuehao Li, Yunyi Jia
Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation. Most research in this area main focus on developing sensing and control approaches to achieve autonomous driving functions such as model based approaches and neural network based approaches. However, even if the autonomous driving functions are ideally achieved, the performance of the system is still subject to sensing exceptions. Few research has studied how to efficiently handle such sensing exceptions. In existing autonomous approaches, sensors, such as cameras, radars and lidars, usually need to be full calibrated or trained after mounted on the vehicles and before being used for autonomous driving. A simple unexpected on the sensors, e.g., mounting position or angle of a camera is changed, may lead the autonomous driving function to fail.
2017-03-28
Technical Paper
2017-01-1379
Yilu Murphey, Dev S. Kochhar, Yongquan Xie, Benjamin Pollatz, Rahul Kulkarni, Yifu Liu, Paul Watta
Drivers often engage in secondary in-vehicle activity that is not related to vehicle control because they believe they can do so safely. Often, it may be to relieve the monotony of driving. Interest is growing to understand and measure a driver’s workload, and design vehicle functionality to accommodate a driver’s perceived, rather than actual, workload. An accurate and real-time variant measure of driver workload that is personalized to an individual driver could be useful in the design of vehicle functionality that can be invoked and brought to the foreground when necessary, or placed in the background when not necessary. In autonomous vehicles where a driver is present as part of the HMI (human-machine interface), this structure could be helpful to better understand the transition from automated to manual driving mode, and vice versa. In this study, the measurement of perceived workload, and its inherent ‘personalized’ connotation was investigated.
2017-03-28
Technical Paper
2017-01-1405
Tzu-Sung Wu, Min-Shiu hsieh PhD, Po-Hsiang Liao, Ping-Min Hsu
Autonomous Emergency Braking Systems (AEBS) usually contain radar, (stereo) camera and/or LiDAR-based technology to identify potential collision partners ahead of the car, such that to warn the driver or automatically brake to avoid or mitigate a crash. The advantage of camera is less cost: however, is inevitable to face the defects of cameras in AEBS, that is, the image recognition cannot perform good accuracy in the poor or over-exposure light condition. Therefore, the compensation of other sensors is of importance. Motivated by the improvement of false detection, we propose a Pedestrian-and-Vehicle Recognition (PVR) algorithm based on radar to apply to AEBS. The PVR employs the radar cross section (RCS) and standard deviation of width of obstacle to determine whether a threshold value of RCS and standard deviation of width of the pedestrian and vehicle is crossed, and to identity that the objective is a pedestrian or vehicle, respectively.
2017-03-28
Technical Paper
2017-01-1401
Trong-Duy Nguyen, Joseph Lull, Satish Vaishnav
In this paper, a method of improving automated vehicle’s perception using a multi-pose camera system (MPCS) is presented. The proposed MPCS is composed of two identical colored and high frame-rate cameras: one installed in driver side and the other in passenger side. Perspective of MPCS varies depending on the width of vehicle type in which MPCS is installed. To increase perspective, we use maximum width of the host vehicle as camera to camera distance for the MPCS. In addition, angular positions of the two cameras in MPCS are controlled by two separate electric motor-based actuators. Steering wheel angle, which is available from vehicle Controller Area Network (CAN) messages, is used to supply information to the actuators to synchronize MPCS camera positions with the host vehicle steering wheel.
2017-03-28
Technical Paper
2017-01-0027
Li Xu, Eric Tseng, Thomas Pilutti, Steven Schondorf
Reversing a vehicle while towing a trailer can be challenging for many drivers, particularly for those who only tow on an occasionally basis. Systems used to assist a driver with backing a trailer typically estimate the heading angle of the trailer relative to that of the vehicle, i.e., the hitch angle. In the current Ford Trailer Backup Assist (TBA) system, the hitch angle is determined utilizing the existing reverse camera with added software in the image processing module. One potential issue for the vision-based hitch angle estimation approach is that environment factors may limit the system usage, since either the camera lenses or the target may be blocked or partially blocked. Furthermore, it is very difficult to apply the vision-based approach to gooseneck or fifth wheel trailers. In this paper, a yaw rate based hitch angle observer is proposed as an alternative sensing solution for TBA.
2017-03-28
Technical Paper
2017-01-0113
Vaclav Jirovsky
Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. The close future is awaiting more systems of the kind and even significant penetration of fully autonomous vehicles in regular traffic is expected by OEMs in Europe around year 2025. The driving is highly multitasking activity and human errors emerge in situations, when he is not able to process and understand the essential amount of information. Future autonomous systems very often rely on some type of inter-vehicular communication. This shall provide the vehicle with similar or higher amount of information, than driver uses in his decision making process. Therefore, currently used, and debatable, 1-D quantity TTC (time-to-collision) will definitely become inadequate. Regardless the vehicle is driven by human or robot, it’s always necessary to know, whether and which reaction is necessary to perform.
2017-03-28
Technical Paper
2017-01-0032
Wei Yang, Ling zheng, Yinong Li, Yue Ren, Yusheng Li
Aiming at the automatic parking system to discontinuous problems, the Sigmoid function is adopted to fit the two section parking path. The transverse preview model is established and the path tracking error is estimated. Then the preview fuzzy control algorithm is adopted to track the ideal path. Finally, PreScan virtual simulation environment is established and the information of parking spaces is obtained by ultrasonic sensors. Research results show that the coefficient of determination can reach over 0.99 between the Sigmoid function and the two-section parking path. Based on the preview fuzzy control, the car can track the planning path successfully and park into parking spaces.
2017-03-28
Technical Paper
2017-01-0102
Mahdi Heydari, Feng Dang, Ankit Goila, Yang Wang, Hanlong Yang
In this paper, a sensor fusion approach is introduced to estimate lane departure. The proposed algorithm combines the camera and inertial navigation sensor data with the vehicle dynamics to estimate the vehicle path and the lane departure time. The lane path and vehicle path are estimated by using extended Kalman filters. This algorithm can be used to provide early warning for lane departure in order to increase driving safety. Additionally, the algorithm can be used to reduce the latency of information embedded in the controls, so that the vehicle lateral control performance can be significantly improved during lane keeping in Advanced Driver Assistance Systems (ADAS) or autonomous vehicles. Furthermore, it improves lane detection reliability in situations when camera fails to detect lanes. Several scenarios are simulated in order to show the effectiveness of the proposed algorithm.
2017-03-28
Technical Paper
2017-01-0045
Guirong Zhuo, Cheng Wu, Fengbo Zhang
Vehicle active collision avoidance includes collision avoidance by braking and by steering, however both of these two methods have their limitations. When the vehicle’s speed is high or road adhesion coefficient is small, critical braking distance is long by braking to avoid collision, and collision avoidance by steering is restricted to the vehicle driving condition on the side lane. Therefore, it is significant to establish the feasible region of active collision avoidance to choose the optimal way to avoid traffic accidents. Model predictive control (MPC), as an optimized method, not only makes the control input of current time to achieve the best, but also can achieve the optimal control input in a future time.
2017-03-28
Technical Paper
2017-01-0040
Michael Hafner, Thomas Pilutti
We propose a steering controller for automated trailer backup, which can be used on tractor-trailer configurations including fifth wheel campers and gooseneck style trailers. The controller steers the trailer based on real-time driver issued trailer curvature commands. We give a stability proof for the hierarchical control system, and demonstrate robustness under a specific set of modeling errors. Simulation results are provided along with experimental data from a test vehicle and 5th wheel trailer.
2017-03-28
Technical Paper
2017-01-0110
Hao Sun, Weiwen Deng, Chen Su, Jian Wu
The ability to recognize traffic vehicles’ lane change maneuver lays the foundation for predicting their long-term trajectories in real-time, which is a key component for Advanced Driver Assistance Systems (ADAS) and autonomous automobiles. Learning-based approach is powerful and efficient, such approach has been used to solve maneuver recognition problems of the ego vehicles on conventional researches. However, since the parameters and driving states of the traffic vehicles are hardly observed by exteroceptive sensors, the performance of traditional methods cannot be guaranteed. In this paper, a novel approach using multi-class probability estimates and Bayesian inference model is proposed for traffic vehicle lane change maneuver recognition. The multi-class recognition problem is first decomposed into three binary problems under error correcting output codes (ECOC) framework.
2017-03-28
Technical Paper
2017-01-0025
Takayuki Kitamura, Naotsugu Shimizu, Yasuyuki Miyake
In the last decade, radar-based Advanced Driver Assistance Systems(ADAS) have improved transportation safety. And today, the standardization of ADAS by NCAP is expected to expand its market globally. One of the key technology for ADAS is rear-side monitoring system enabling application such as Blind Spot Warning(BSW) and Lane Change Warning(LCW). It is desired to expand the detection range so that it can monitor not only nearside targets widely for BSW but farther targets for LCW. These applications can be achieved by using two radar sensors installed at rear-side corner of the vehicle. However, wider and farther detection range causes the undesirable target detection and decreases target recognition performance. In this paper, a novel solution to improve the performance by using 2FCW-based Beamspace technology is introduced. Combined with IQ detector, 2FCW receiver outputs beat signal proportional to the relative speed of an object.
2017-03-28
Technical Paper
2017-01-1403
Alexander Koenig, Michael Gutbrod, Sören Hohmann, Julian Ludwig
Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundred millions of test kilometers without incidents to show that statistically HAD is significantly safer. One solution is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenes is fused in a world model and enhanced by incorporating future time steps into original measurements.
2017-03-28
Technical Paper
2017-01-1441
Heungseok Chae, Kyong Chan Min, Kyongsu Yi
This paper describes design and evaluation of a driving mode decision and lane change control algorithm of automated vehicle in merge situations on highway intersection. For the development of a highly automated driving control algorithm in merge situation, driving mode change from lane keeping to lane change is necessary to merge appropriately. In a merge situation, the driving objective is slightly different to general driving situation. Unlike general situation, the lane change should be completed in a limited travel distance in a merge situation. Merge mode decision is determined based on surrounding vehicles states and remained merge lane length. In merge mode decision algorithm, merge availability and desired merge position are decided to change lane safely and quickly. Merge availability and desired merge position are based on the safety distance that considers relative velocity and relative position of subject and surrounding vehicles.
2017-03-28
Technical Paper
2017-01-0030
Shunsuke Kogure, Takashi Kato, Shin Osuga
With the improved safety performance of vehicles, the number of accidents have been reducing than before. However, accidents due to driver distraction still remain to occur, and thus there could be a relatively high need to determine whether a driver properly looks at the surroundings or not. Meanwhile, in the wake of the trend of partial automatic driving of vehicles in recent years, it is also highly required to grasp the state of a driver, and regardless of whether it is automatic driving or not it is sought to grasp the state of a driver and perform application for control depending on the state of the driver. Under the circumstance, we have built an algorithm that determines a gazing point of a driver for performing basic determination of whether or not the driver is in a state suitable for safe driving of a vehicle.
2017-03-28
Technical Paper
2017-01-0043
Michael Smart, Satish Vaishnav, Steven Waslander
Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal equipment. In previous work on stereo images, dynamic Bayesian networks with heuristic features were used whose distributions are identified using unsupervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. The situations where poor RANSAC hypotheses were generated and significantly contributed to false alarms have been corrected. The null hypothesis is reformulated to guarantee that detected hypothesis satisfy a minimum likelihood. The computational requirements have been reduced for tracking and pairing by computing an upper bound on the marginal likelihood of all part hypotheses and rejecting part hypothesis if its upper bound is less likely than the null hypothesis.
2017-03-28
Technical Paper
2017-01-1555
Mirosław Jan Gidlewski, Krystof JANKOWSKI, Andrzej MUSZYŃSKI, Dariusz ŻARDECKI
Lane change automation appears to be a fundamental problem of vehicle automated control, especially when the vehicle is driven at high speed. Selected relevant parts of the recent research project are reported in this paper, including literature review, the developed models and control systems, as well as crucial simulation results. In the project, two original models describing the dynamics of the controlled motion of the vehicle were used, verified during the road tests and in the laboratory environment. The first model – fully developed (multi-mass, 3D, nonlinear) – was used in simulations as a virtual plant to be controlled. The second model – a simplified reference model of the lateral dynamics of the vehicle (single-mass, 2D, linearized) – formed the basis for theoretical analysis, including the synthesis of the algorithm for automatic control. That algorithm was based on the optimal control theory.
2017-03-28
Technical Paper
2017-01-0072
Yang Zheng, Navid Shokouhi, Amardeep Sathyanarayana, John Hansen
The proliferation of smartphone application has made a great impact in the automotive industry. Smartphones contain a variety of useful sensors including cameras, microphones, as well as their Inertial Measurement Units (IMU) such as accelerometer, gyroscope, and GPS. These multi-channel signals would also be synchronized to provide a comprehensive description of driving scenarios. Therefore, the smartphone could potentially be leveraged for in-vehicle data collection, monitoring, and added safety options/feedback strategies. In our previous study, a smartphone/tablet solution with our Android App - MobileUTDrive - was developed. This platform provides a cost effective approach, which allows for a wider range of naturalistic driving study opportunities for drivers operating their own vehicles. The most meaningful reason for introducing the smartphone platform is its potential ability to be integrated with intelligent telematics services.
2017-03-28
Technical Paper
2017-01-1409
Markus Schratter, Susie Cantu, Thomas Schaller, Peter Wimmer, Daniel Watzenig
Highly Automated Driving opens up new middle-term perspectives in mobility and is therefore currently one of the main goals in the development of future vehicles. The focus is the implementation of Highly Automated Driving functions for structured environments, such as on the motorway. To achieve this goal, the vehicles are equipped with additional technologies (redundant surround sensing, highly precision digital maps, driver monitoring, etc.). These technologies shouldn’t only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving, to increase the road safety. Two potential uses will be discussed in this paper. 1) In the first approach we investigated the usage of machine learning to develop a braking strategy for AEB systems.
2017-03-28
Technical Paper
2017-01-0104
Maryam Moosaei, Simon Smith, Madeline J. Goh, Vidya Nariyambut Murali, Yi Zhang, Ashley Micks
Traffic light detection is critical for safe behavior in a world where technology on vehicles is growing more complex. In this work we outline a deep learning based solution for traffic light detection that leverages virtual data for affordable and efficient supervised learning. Using Unreal Engine, we generated a virtual dataset by moving a virtual camera through a variety of intersection scenes while varying parameters such as lighting, camera position and angle. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network (DNN), without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Using color space conversion and contour extraction, we identified candidate regions by filtering based on color, shape and size.
2017-03-28
Technical Paper
2017-01-0050
Mario Berk, Hans-Martin Kroll, Olaf Schubert, Boris Buschardt, Daniel Straub
With increasing levels of driving automation, the information provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensor’s reliability is therefore crucial for ensuring the safety of the customer functions. There are currently no standardized procedures or guidelines for demonstrating the reliability of the sensor information. Engineers are faced with setting up test procedures and estimating efforts. Statistical hypothesis tests are commonly employed in this context. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. It also enables a more realistic representation of dependencies among errors.
2017-03-28
Technical Paper
2017-01-0052
Andre Kohn, Rolf Schneider, Antonio Vilela, Udo Dannebaum, Andreas Herkersdorf
The reliability of safety-related systems is a primary goal of automotive ECU development and hence an essential part in developing ECU architectures. Nowadays this becomes even more challenging due to the increasing complexity of algorithms and assistance systems that are already able to manage numerous driving tasks on their own. This trend will be additionally intensified by the growing degree of automated driving, continually reducing the need for manual driver intervention. But the absence of a human driver as a fallback leads to the replacement of fail-safe architectures with new fail-operational approaches to maintain control of vehicles in case of errors. In our contribution we will point out an exemplary approach to analyze the reliability of such a fail-operational concept on ECU level. For this, we consider a typical safety-related ECU from the chassis domain, used as a basis for enhancing it to facilitate fail-operational capabilities on a single ECU.
2017-03-28
Technical Paper
2017-01-0429
Michael Holland, Jonathan Gibb, Kacper Bierzanowski, Stuart Rowell, Bo Gao, Chen Lv, Dongpu Cao
This paper outlines the procedure used to assess the performance of a Lane Keeping Assistance System (LKAS) in a virtual test environment using the newly developed Euro NCAP Lane Support Systems (LSS) Test Protocol. A method to automate the testing and analysis of this test is also outlined. The Euro NCAP LSS Test defines test paths for left lane departures and ten for right lane departures that must be followed by the vehicle before the LKAS activates. Each path must be followed to within a specific tolerance. The driver inputs required to ensure the vehicle follows the test path are calculated. These tests are then run concurrently in the virtual environment by combining two different software packages. Important vehicle variables are recorded and processed, and a pass/fail status is assigned to each test automatically. Any vehicle with a LKAS, and a validated parameter set can therefore be tested and analysed automatically using this method.
2017-03-28
Technical Paper
2017-01-0069
Venkatesh Raman, Mayur Narsude, Damodharan Padmanaban
This paper describes main challenges encountered during data enrichment phase of connected vehicle experiments. It also compares data imputation approaches for data coming from actual driving scenarios and obtained using in-vehicle data acquisition devices. Three distinct window-based approaches were used for cleaning and imputing the missing values in different CAN-bus (Controller Area Network) signals. Lengths of windows used for data imputation for the three approaches were: 1) entire time-course, 2) day, and 3) trip (defined as duration between vehicle engine ON to OFF). An algorithm for identification of engine ON and OFF events will also be presented, in case this signal is not explicitly captured during the data acquisition phase. As a case study, these imputation techniques were applied to the data from vehicle’s CAN information in a driver behavior classification experiment.
2017-03-28
Technical Paper
2017-01-0047
Jie Bai, Sihan CHEN, Hua Cui PhD, Xin Bi, Libo Huang
The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.
2017-03-28
Technical Paper
2017-01-0432
Bing Zhu, Zhipeng Liu, Jian Zhao, Weiwen Deng
Adaptive cruise control system with lane change assistance (LCACC)is a novel advanced driver assistance system (ADAS), which enables dual-target tracking, safe lane change, and longitudinal ride comfort. To design the personalized LCACC system, one of the most important prerequisite is to identify the driver’s individualities. This paper presents a driver behavior characteristics identification strategy for LCACC based on a dynamics driver model. Firstly, a driver behavior data acquisition system was established using the dSPACE real-time simulation platform, and different types of driver behavior data were collected under the typical test condition. Then, driver behavior characteristics were analyzed and identified based on the dynamics driver model, which combined the longitudinal and lateral control behavior. Finally, the proposed identification strategy was verified by the driver-in-the-loop simulator.
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