Criteria

Text:
Display:

Results

Viewing 1 to 30 of 737
2017-06-05
Technical Paper
2017-01-1781
Joshua Wheeler
Abstract The design and operation of a vehicle’s heating, ventilation, and air conditioning (HVAC) system has great impact on the performance of the vehicle’s Automatic Speech Recognition (ASR) and Hands-Free Communication (HFC) system. HVAC noise provides high amplitudes of broadband frequency content that affects the signal to noise ratio (SNR) within the vehicle cabin, and works to mask the user’s speech. But what’s less obvious is that when the airflow from the panel vents or defroster openings can be directed toward the vehicle microphone, a mechanical “buffeting” phenomenon occurs on the microphone’s diaphragm that distresses the ASR system beyond its ability to interpret the user’s voice. The airflow velocity can be strong enough that a simple windscreen on the microphone is not enough to eliminate the problem. Minimizing this buffeting effect is a vital key to building a vehicle that meets the customer’s expectations for ASR and HFC performance.
2017-06-05
Technical Paper
2017-01-1853
SangHeon Lee, TaeHun Kim, SeungHwan Shin, YangNam Lim
Abstract Knowledge of the vehicle mass is an important factor to measure the tire inflation pressure indirectly. To estimate the mass change from the wheel speed signals, the novel zero crossing method (ZCM) was proposed. The accuracy of the proposed method was demonstrated using the logged vehicle data, and the compatibility with indirect tire pressure monitoring system (iTPMS) was evaluated by statistical analysis. Therefore, the proposed ZCM for vehicle mass estimation can expect technological advances in iTPMS and chassis control systems.
2017-05-10
Technical Paper
2017-01-1931
Christian Ballarin, Martin Zeilinger
Due to the continuous increasing highway transport and the decreasing investments into infrastructure a better usage of the installed infrastructure is indispensable. Therefore the operation and interoperation of assistance and telematics systems become more and more necessary. Regarding these facts Highway Pilot was developed at Daimler Trucks. The Highway Pilot System moves the truck highly automated and independent from other road users within the allowed speed range and the required security distance. Daimler Trucks owns diverse permissions in Germany and the USA for testing these technologies on public roads. Next generation is the Highway Pilot Connect System that connects three highly automated driving trucks. The connection is established via Vehicle-to-Vehicle communication (V2V).
2017-04-11
Journal Article
2017-01-9627
André Lundkvist, Roger Johnsson, Arne Nykänen, Jakob Stridfelt
Abstract The objective of this study was to investigate if 3D auditory displays could be used to enhance parking assistance systems (PAS). Objective measurements and estimations of workload were used to assess the benefits of different 3D auditory displays. In today’s cars, PAS normally use a visual display together with simple sound signals to inform drivers of obstacles in close proximity. These systems rely heavily on the visual display, as the sound does not provide information about obstacles' location. This may cause the driver to lose focus on the surroundings and reduce situational awareness. Two user studies (during summer and winter) were conducted to compare three different systems. The baseline system corresponded to a system normally found in today’s cars. The other systems were designed with a 3D auditory display, conveying information of where obstacles were located through sound. A visual display was also available.
2017-03-28
Technical Paper
2017-01-1471
Xiao Luo, Wenjing Du, Hao Li, Peiyu LI, Chunsheng Ma, Shucai Xu, Jinhuan Zhang
Abstract Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse.
2017-03-28
Technical Paper
2017-01-1727
Yumin Lin, Bo-Chiuan Chen, Hsien-Chi Tsai, Bi-Cheng Luan
Abstract A model-based sensor fault detection algorithm is proposed in this paper to detect and isolate the faulty sensor. Wheel speeds are validated using the wheel speed deviations before being employed to check the sensor measurements of the vehicle dynamics. Kinematic models are employed to estimate yaw rate, lateral acceleration, and steering wheel angle. A Kalman filter based on a point mass model is employed to estimate longitudinal speed and acceleration. The estimated vehicle dynamics and sensor measurements are used to calculate the residuals. Adaptive threshold values are employed to identify the abnormal increments of residuals. Recursive least square method is used to design the coefficients of the expressions for adaptive threshold values, such that the false alarms caused by model uncertainties can be prevented. Different combinations of estimations are employed to obtain 18 residuals.
2017-03-28
Technical Paper
2017-01-1555
Mirosław Jan Gidlewski, Krystof JANKOWSKI, Andrzej MUSZYŃSKI, Dariusz ŻARDECKI
Abstract 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-body, 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-body, 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
2017-01-1382
Michelle L. Reyes, Cheryl A. Roe, Ashley B. McDonald, Julia E. Friberg, Daniel V. McGehee
Abstract Advanced driver assistance systems (ADAS) show tremendous promise for increasing safety on our roadways. However, while these technologies are rapidly infiltrating the American passenger vehicle market, many consumers have little to no experience or knowledge of them prior to getting behind the wheel. The Technology Demonstration Study was conducted to evaluate how the ways in which drivers learn about ADAS affect their perceptions of the technologies. This paper investigates drivers’ knowledge of the purpose, function, and limitations of the advanced driver assistance technology of adaptive cruise control (ACC), along with ratings of perceived usefulness, apprehension, and effort required to learn to use ACC.
2017-03-28
Technical Paper
2017-01-0068
Pablo Sauras-Perez, Andrea Gil, Jasprit Singh Gill, Pierluigi Pisu, Joachim Taiber
Abstract In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.
2017-03-28
Technical Paper
2017-01-0067
Wei Han, Xinyu Zhang, Jialun Yin, Yutong Li, Deyi Li
Abstract Safety of buses is crucial because of the large proportion of the public transportation sector they constitute. To improve bus safety levels, especially to avoid driver error, which is a key factor in traffic accidents, we designed and implemented an intelligent bus called iBus. A robust system architecture is crucial to iBus. Thus, in this paper, a novel self-driving system architecture with improved robustness, such as to failure of hardware (including sensors and controllers), is proposed. Unlike other self-driving vehicles that operate either in manual driving mode or in self-driving mode, iBus offers a dual-control mode. More specifically, an online hot standby mechanism is incorporated to enhance the reliability of the control system, and a software monitor is implemented to ensure that all software modules function appropriately. The results of real-world road tests conducted to validate the feasibility of the overall system confirm that iBus is reliable and robust.
2017-03-28
Technical Paper
2017-01-0070
Longxiang Guo, Sagar Manglani, Xuehao Li, Yunyi Jia
Abstract Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation systems. Most research in this area has mainly been focused on developing sensing and control approaches to achieve various autonomous driving functions. Very little of this research, however, has studied how to efficiently handle sensing exceptions. A simple exception measured by any of the sensors may lead to failures in autonomous driving functions. The autonomous vehicles are then supposed to be sent back to manufacturers for repair, which takes both time and money. This paper introduces an efficient approach to make human drivers able to online teach autonomous vehicles to drive under sensing exceptions. A human-vehicle teaching-and-learning framework for autonomous driving is proposed and the human teaching and vehicle learning processes for handling sensing exceptions in autonomous vehicles are designed in detail.
2017-03-28
Technical Paper
2017-01-0069
Venkatesh Raman, Mayur Narsude, Damodharan Padmanaban
Abstract This manuscript compares window-based data imputation approaches for data coming from connected vehicles during actual driving scenarios and obtained using on-board data acquisition devices. Three distinct window-based approaches were used for cleansing 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 for each vehicle ID, 2) day, and 3) trip (defined as duration between vehicle's ignition statuses ON to OFF). An algorithm for identification of ignition ON and OFF events is also presented, since this signal was not explicitly captured during the data acquisition phase. As a case study, these imputation techniques were applied to the data from a driver behavior classification experiment.
2017-03-28
Journal Article
2017-01-0052
Andre Kohn, Rolf Schneider, Antonio Vilela, Udo Dannebaum, Andreas Herkersdorf
Abstract A main challenge when developing next generation architectures for automated driving ECUs is to guarantee reliable functionality. Today’s fail safe systems will not be able to handle electronic failures due to the missing “mechanical” fallback or the intervening driver. This means, fail operational based on redundancy is an essential part for improving the functional safety, especially in safety-related braking and steering systems. The 2-out-of-2 Diagnostic Fail Safe (2oo2DFS) system is a promising approach to realize redundancy with manageable costs. In this contribution, we evaluate the reliability of this concept for a symmetric and an asymmetric Electronic Power Steering (EPS) ECU. For this, we use a Markov chain model as a typical method for analyzing the reliability and Mean Time To Failure (MTTF) in majority redundancy approaches. As a basis, the failure rates of the used components and the microcontroller are considered.
2017-03-28
Technical Paper
2017-01-0116
Ankit Goila, Ambarish Desai, Feng Dang, Jian Dong, Rahul Shetty, Rakesh Babu Kailasa, Mahdi Heydari, Yang Wang, Yue Sun, Manikanta Jonnalagadda, Mohammed Alhasan, Hanlong Yang, Katherine R. Lastoskie
ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.
2017-03-28
Technical Paper
2017-01-0113
Vaclav Jirovsky
Abstract Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. More systems of the kind and even fully autonomous vehicles in regular traffic are expected by OEMs in Europe around year 2025. Driving is highly multitasking activity and human errors emerge in situations, when he is unable 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 higher amount of information, than driver uses in his decision making process. Therefore, currently used 1-D quantity TTC (time-to-collision) will 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. Adaptable autonomous vehicle systems will need to analyze the driver’s situation awareness level.
2017-03-28
Technical Paper
2017-01-0117
Raja Sekhar Dheekonda, Sampad Panda, Md Nazmuzzaman khan, Mohammad Hasan, Sohel Anwar
Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms.
2017-03-28
Technical Paper
2017-01-0104
Maryam Moosaei, Yi Zhang, Ashley Micks, Simon Smith, Madeline J. Goh, Vidya Nariyambut Murali
Abstract In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, 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. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size.
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, inertial navigation sensor, and GPS 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 Kalman filters. This algorithm can be used to provide early warning for lane departure in order to increase driving safety. By integrating inertial navigation sensor and GPS data, the inertial sensor biases can be estimated and the vehicle path can be estimated where the GPS data is not available or is poor. 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.
2017-03-28
Technical Paper
2017-01-0110
Hao Sun, Weiwen Deng, Chen Su, Jian Wu
Abstract 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
2017-01-0091
Songyao Zhou, Gangfeng Tan, Kangping Ji, Renjie Zhou, Hao Liu
Abstract The mountainous roads are rugged and complex, so that the driver can not make accurate judgments on dangerous road conditions. In addition, most heavy vehicles have characteristics of large weight and high center of gravity. The two factors above have caused most of the car accidents in mountain areas. A research shows that 90% of car accidents can be avoided if drivers can respond within 2-3 seconds before the accidents happen. This paper proposes a speed warning scheme for heavy-duty vehicle over the horizon in mountainous area, which can give the drivers enough time to respond to the danger. In the early warning aspect, this system combines the front road information, the vehicle characteristics and real-time information obtained from the vehicle, calculates and forecasts the danger that may happen over the horizon ahead of time, and prompts the driver to control the vehicle speed.
2017-03-28
Technical Paper
2017-01-0096
Valentin Soloiu, Bernard Ibru, Thomas Beyerl, Tyler Naes, Charvi Popat, Cassandra Sommer, Brittany Williams
Abstract An important aspect of an autonomous vehicle system, aside from the crucial features of path following and obstacle detection, is the ability to accurately and effectively recognize visual cues present on the roads, such as traffic lanes, signs and lights. This ability is important because very few vehicles are autonomously driven, and must integrate with conventionally operated vehicles. An enhanced infrastructure has yet to be available solely for autonomous vehicles to more easily navigate lanes and intersections non-visually. Recognizing these cues efficiently can be a complicated task as it not only involves constantly gathering visual information from the vehicle’s surroundings, but also requires accurate real time processing. Ambiguity of traffic control signals challenges even the most advanced computer decision making algorithms. The vehicle then must keep a predetermined position within its travel lane based on its interpretation of its surroundings.
2017-03-28
Technical Paper
2017-01-0071
Vahid Taimouri, Michel Cordonnier, Kyoung Min Lee, Bryan Goodman
Abstract While operating a vehicle in either autonomous or occupant piloted mode, an array of sensors can be used to guide the vehicle including stereo cameras. The state-of-the-art distance map estimation algorithms, e.g. stereo matching, usually detect corresponding features in stereo images, and estimate disparities to compute the distance map in a scene. However, depending on the image size, content and quality, the feature extraction process can become inaccurate, unstable and slow. In contrast, we employ deep convolutional neural networks, and propose two architectures to estimate distance maps from stereo images. The first architecture is a simple and generic network that identifies which features to extract, and how to combine them in a multi-resolution framework.
2017-03-28
Technical Paper
2017-01-0072
Yang Zheng, Navid Shokouhi, Amardeep Sathyanarayana, John Hansen
Abstract With the embedded sensors – typically Inertial Measurement Units (IMU) and GPS, the smartphone could be leveraged as a low-cost sensing platform for estimating vehicle dynamics. However, the orientation and relative movement of the smartphone inside the vehicle yields the main challenge for platform deployment. This study proposes a solution of converting the smartphone-referenced IMU readings into vehicle-referenced accelerations, which allows free-positioned smartphone for the in-vehicle dynamics sensing. The approach is consisted of (i) geometry coordinate transformation techniques, (ii) neural networks regression of IMU from GPS, and (iii) adaptive filtering processes. Experiment is conducted in three driving environments which cover high occurrence of vehicle dynamic movements in lateral, longitudinal, and vertical directions. The processing effectiveness at five typical positions (three fixed and two flexible) are examined.
2017-03-28
Journal Article
2017-01-0073
Andreas Barthels, Christian Ress, Martin Wiecker, Manfred Müller
Abstract Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.
2017-03-28
Technical Paper
2017-01-0076
Modar Horani, Ghaith Al-Refai, Osamah Rawashdeh
Abstract Current implementations of vision-based Advanced Driver Assistance Systems (ADAS) are largely dependent on real-time vehicle camera data along with other sensory data available on-board such as radar, ultrasonic, and GPS data. This data, when accurately reported and processed, helps the vehicle avoid collisions using established ADAS applications such as Forward Collision Avoidance (FCA), Autonomous Cruise Control (ACC), Pedestrian Detection, etc. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) over Dedicated Short Range Communication (DSRC) provides basic sensory data from other vehicles or roadside infrastructure including position information of surrounding traffic. Exchanging rich data such as vision data between multiple vehicles, and between vehicles and infrastructure provides a unique opportunity to advance driver assistance applications and Intelligent Transportation Systems (ITS).
2017-03-28
Technical Paper
2017-01-0078
Alexander Katriniok, Peter Kleibaum, Christian Ress, Lutz Eckstein
Abstract Today, automated vehicles mostly rely on ego vehicle sensors such as cameras, radar or LiDAR sensors that are limited in their sensing capability and range. Vehicle-to-everything (V2X) communication has the potential to appropriately complement these sensors and even allow for a cooperative, proactive interaction of vehicles. As such, V2X communication might play a vital role on the way to smart and efficient traffic solutions. In the public funded research project UK Autodrive, we are currently investigating and experimentally evaluating V2X-based applications based on dedicated short range communication (DSRC). Moreover, the novel application intersection priority management (IPM) is part of the research project. IPM aims at automating intersections in such a way that vehicles can pass safely and even more efficiently without the use of traffic lights or signs.
2017-03-28
Technical Paper
2017-01-0433
Yang Xing, Chen Lv, Wang Huaji, Hong Wang, Dongpu Cao
Abstract Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.
2017-03-28
Technical Paper
2017-01-0432
Bing Zhu, Zhipeng Liu, Jian Zhao, Weiwen Deng
Abstract 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 prerequisites is to identify the driver’s individualities. This paper presents a real-time driver behavior characteristics identification strategy for LCACC system. Firstly, a driver behavior data acquisition system was established based on the driver-in-the-loop simulator, and the behavior data of different types of drivers were collected under the typical test condition. Then, the driver behavior characteristics factor Ks we proposed, which combined the longitudinal and lateral control behaviors, was used to identify the driver behavior characteristics. And an individual safe inter-vehicle distances field (ISIDF) was established according to the identification results.
2017-03-28
Technical Paper
2017-01-0429
Michael Holland, Jonathan Gibb, Kacper Bierzanowski, Stuart Rowell, Bo Gao, Chen Lv, Dongpu Cao
Abstract 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, version 1.0, November 2015 [1]. A tool has also been developed to automate the testing and analysis of this test. The Euro NCAP LSS Test defines ten 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 vehicle control inputs required to follow 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 based on these values automatically.
2017-03-28
Technical Paper
2017-01-1653
Jon Barton Shields, Jörg Huser, David Gell
Abstract This paper discusses the merits, benefits and usage of autonomous key management (with implicit authentication) (AKM) solutions for securing ECU-to-ECU communication within the connected vehicle and IoT applications; particularly for transmissions between externally exposed, edge ECU sensors connected to ECUs within the connected vehicle infrastructure. Specific benefits addressed include reductions of communication latency, implementation complexity, processing power and energy consumption. Implementation issues discussed include provisioning, key rotation, synchronization, re-synchronization, digital signatures and enabling high entropy.
Viewing 1 to 30 of 737

Filter

Subtopics