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Viewing 1 to 30 of 551
2017-09-23
Technical Paper
2017-01-1964
Xiangkun He, Xuewu Ji, Kaiming Yang, Yulong Liu, Jian WU, Yahui Liu
Abstract Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.
2017-09-23
Technical Paper
2017-01-1975
Wenhui Li, Wenlan Li, Jialun Liu, Yuhao Chen
Abstract Vehicle detection has been a fundamental problem in the research of Intelligent Traffic System (ITS), especially in urban driving environment. Over the past decades, vision-based vehicle detection has got a considerable attention. In addition to the rich appearance information, the stereo vision method also provides depth information, which could achieve higher accuracy and precision. In this paper, a hybrid method for stereo vision-based real-time vehicle detection in urban environment is proposed. Firstly, we extract vehicle features and generate the Region of Interest (ROI). Semi-global Matching (SGM) algorithm is then utilized on the ROIs to generate disparity maps and get the depth information, which could be used to compute the width of each ROI. The noise regions, always with obvious depth variation in the disparity maps are excluded by the clustering approach.
2017-09-23
Technical Paper
2017-01-1978
Yuxiang Feng, Simon Pickering, Edward Chappell, Pejman iravani PhD, Chris Brace
Abstract The major contribution of this paper is to propose a low-cost accurate distance estimation approach. It can potentially be used in driver modelling, accident avoidance and autonomous driving. Based on MATLAB and Python, sensory data from a Continental radar and a monocular dashcam were fused using a Kalman filter. Both sensors were mounted on a Volkswagen Sharan, performing repeated driving on a same route. The established system consists of three components, radar data processing, camera data processing and data fusion using Kalman filter. For radar data processing, raw radar measurements were directly collected from a data logger and analyzed using a Python program. Valid data were extracted and time stamped for further use. Meanwhile, a Nextbase monocular dashcam was used to record corresponding traffic scenarios. In order to measure headway distance from these videos, object depicting the leading vehicle was first located in each frame.
2017-09-23
Technical Paper
2017-01-1990
Xiangyu huang, Hao Zhou
Abstract The most important role of V2X technology is to significantly enhance driving safety. This paper proposes an Omni-directional collision warning method based on vehicle to vehicle communication. With the Basic Safety Message (BSM), the driving states of vehicles which communicate with host vehicle can be obtained. The warnings are divided into two categories based on the Lateral Offset calculation: forward collision warning (FCW) for vehicles moving in the same direction and cross collision warning (CCW) for vehicles moving in different directions. For vehicles which moves in the same direction, the lateral offset of the two vehicles, the time to collision (TTC) and time headway (THW) are used to estimate forward collision risk. For vehicles which moves in different directions, time to the closest point approach (TCPA) model and the separating axis theorem (SAT) are used for cross collision detection.
2017-09-23
Technical Paper
2017-01-1998
Shun Yang, Weiwen Deng, Zhenyi Liu, Ying Wang
Abstract Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.
2017-09-23
Technical Paper
2017-01-2009
Kuiyuan Guo, Yan Yan, Juan Shi, Runqing Guo, Yuguang Liu
Abstract In order to speed up the development of vehicle active safety technology in China, C-NCAP plans to add AEB and AEB VRU system as assessment items in 2018. With the purpose of studying the assessment protocol of AEB system, we have carried out 400,000 km road information collection and then we acquired the statistics of the operation conditions of dangerous situations. Combined with the traffic accident data collected by CIDAS, we found that the dangerous situations that we usually met were mainly three types, that was CCRs, CCRm and CCRb. Based on what we mentioned above, we analyzed the three kinds of working conditions and gave the corresponding evaluation method. In addition, combined with the actual situation of China, we added two tests of error function. And then we took the actual road experiment of many models of vehicles.
2017-09-23
Technical Paper
2017-01-1958
Dongfang Dang
Abstract With the increasing complexity, dynamicity and uncertainty of traffic, motion planning of automatic driving is getting more difficult and challenging. This paper focuses on the real-time motion planning problem of CAVs (connected and automated vehicles) in complex traffic scenarios. To effectively solve this problem, a general driving risk model is presented, which contains the following two essential parts: i) collision risk, i.e., the collision risk between the SV (subject vehicle) and other surrounding vehicles, pedestrians, buildings etc.; ii) non-collision risk, such as violation of traffic regulations, the deviation from the intention of driver, etc. To achieve the real time collision detection, the SV is approximated to a point and its shape is considered by extending the dimension of obstacles considering their relative position and velocity.
2017-09-23
Journal Article
2017-01-1960
Xiaopeng Zong, Guoyan Xu, Guizhen Yu, Hongjie Su, Chaowei Hu
Abstract Obstacle avoidance is an important function in self-driving vehicle control. When the vehicle move from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. There are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. In this paper reinforcement learning is applied to the problem to form effective strategies. There are two major challenges that make self-driving vehicle different from other robotic tasks. Firstly, in order to control the vehicle precisely, the action space must be continuous which can’t be dealt with by traditional Q-learning. Secondly, self-driving vehicle must satisfy various constraints including vehicle dynamics constraints and traffic rules constraints. Three contributions are made in this paper.
2017-09-19
Technical Paper
2017-01-2109
Kiran Thupakula
Abstract Airport environments consist of several moving objects both in the air and on the ground. In air moving objects include aircraft, UAVs and birds etc. On ground moving objects include aircraft, ground vehicles and ground personnel etc. Detecting, classifying, identifying and tracking these objects are necessary for avoiding collisions in all environmental situations. Multiple sensors need to be employed for capturing the object shape and position from multiple directions. Data from these sensors are combined and processed for object identification. In current scenario, there is no comprehensive traffic monitoring system that uses multisensor data for monitoring in all the airport areas. In this paper, for explanation purposes, a hypothetical airport traffic monitoring system is presumed that uses multiple sensors for avoiding collisions.
2017-06-05
Technical Paper
2017-01-1778
Enrico Galvagno, Antonio Tota, Mauro Velardocchia, Alessandro Vigliani
Abstract This paper explores the potentiality of reducing noise and vibration of a vehicle transmission thanks to powertrain control integration with active braking. Due to external disturbances, coming from the driver, e.g. during tip-in / tip-out maneuvers, or from the road, e.g. crossing a speed bump or driving on a rough road, the torsional backlashes between transmission rotating components (gears, synchronizers, splines, CV joints), may lead to NVH issues known as clonk. This study initially focuses on the positive effect on transmission NVH performance of a concurrent application of a braking torque at the driving wheels and of an engine torque increase during these maneuvers; then a powertrain/brake integrated control strategy is proposed. The braking system is activated in advance with respect to the perturbation and it is deactivated immediately after to minimize losses.
2017-05-30
Technical Paper
2017-01-5002
James Bradley Skarie
Abstract Antilock braking systems (ABS) are inherently limited by the static coefficient of friction (µ) between a vehicle’s tires and the road surface. This paper explores a unique active safety concept, Integrated Coefficient Enhancement (ICE), which works to improve ABS well beyond their present limits. The ICE concept was developed using a basic physics principle: to change µ between two surfaces, at least one of the surfaces must be altered in some way. By quickly deploying a specially designed tractive medium (TM) to aid in directional stability and braking, hazardous situations can be greatly mitigated. This paper describes the features and testing results of this TM and its aerodynamic-mechanical-electronic deployment apparatus. Under all slippery road conditions tested, the developed TM mitigated skidding, with improvements that ranged from 20% to several hundred percent, depending on conditions and deployment rates.
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
2017-01-1406
Changliu Liu, Jianyu Chen, Trong-Duy Nguyen, Masayoshi Tomizuka
Abstract Road safety is one of the major concerns for automated vehicles. In order for these vehicles to interact safely and efficiently with the other road participants, the behavior of the automated vehicles should be carefully designed. Liu and Tomizuka proposed the Robustly-safe Automated Driving system (ROAD) which prevents or minimizes occurrences of collisions of the automated vehicle with other road participants while maintaining efficiency. In this paper, a set of design principles are elaborated as an extension of the previous work, including robust perception and cognition algorithms for environment monitoring and high level decision making and low level control algorithms for safe maneuvering of the automated vehicle.
2017-03-28
Technical Paper
2017-01-1408
Satoshi Kozai, Yoshihiko Takahashi, Akihiro Kida, Takayuki Hiromitsu, Shinji Kitaura, Sadamasa Sawada, Gladys Acervo, Marius Ichim
Abstract A Rear Cross Traffic Auto Brake (RCTAB) system has been developed that uses radar sensors to detect vehicles approaching from the right or left at the rear of the driver’s vehicle, and then performs braking control if the system judges that a collision may occur. This system predicts the intersecting course of approaching vehicles and uses the calculated time-to-collision (TTC) to control the timing of automatic braking with the aim of helping prevent unnecessary operation while ensuring system performance.
2017-03-28
Technical Paper
2017-01-1400
Keyu Qian, Gangfeng Tan, Renjie Zhou, Binyu Mei, Wanyang XIA
Abstract Downhill mountain roads are the accident prone sections because of their complexity and variety. Drivers rely more on driving experience and it is very easy to cause traffic accidents due to the negligence or the judgment failure. Traditional active safety systems, such as ABS, having subjecting to the driver's visual feedback, can’t fully guarantee the downhill driving safety in complex terrain environments. To enhance the safety of vehicles in the downhill, this study combines the characteristics of vehicle dynamics and the geographic information. Thus, through which the drivers could obtain the safety speed specified for his/her vehicle in the given downhill terrains and operate in advance to reduce traffic accidents due to driver's judgment failure and avoid the brake overheating and enhance the safety of vehicles in the downhill.
2017-03-28
Technical Paper
2017-01-1399
Bin Wu, Xichan Zhu, Jianping Shen, Xuejun Cang, Lin li
Abstract A driver steering model for emergency lane change based on the China naturalistic driving data is proposed in this paper. The steering characteristic of three phases is analyzed. Using the steering primitive fitting by Gaussian function, the steering behaviors in collision avoidance and lateral movement phases can be described, and the stabilization steering principle of yaw rate null is found. Based on the steering characteristic, the near and far aim point used in steering phases is analyzed. Using the near and far aim point correction model, a driver steering model for emergency lane change is established. The research results show that the driver emergency steering model proposed in this paper performs well when explaining realistic steering behavior, and this model can be used in developing the ADAS system.
2017-03-28
Technical Paper
2017-01-1730
Gridsada Phanomchoeng, Sunhapos Chantranuwathana
Abstract Nowadays, the tendency of people using bicycles as the way of transportation has increased as well as the tendency of the bicycle accidents. According to the research of National Highway Traffic Safety Administration (NHTSA), National Survey on Bicyclist and Pedestrian Attitude and Behavior, the major root causes of bicycle accidents are from the road surface condition. Thus, this work has developed the system to detect the road surface condition. The system utilizes the laser and camera to measure the height of road. Then, with the information of the road height and bicycle speed, the road surface condition can be classified into 3 categories due to severe condition of the road. For the secure road, cyclists could safely ride on it. For the warning road, cyclists need to slow down the speed. Lastly, for the dangerous road, cyclists have to stop their bicycles.
2017-03-28
Technical Paper
2017-01-1407
Helene G. Moorman, Andrea Niles, Caroline Crump, Audra Krake, Benjamin Lester, Laurene Milan, Christy Cloninger, David Cades, Douglas Young
Abstract Lane Departure Warning (LDW) systems, along with other types of Advanced Driver Assistance Systems (ADAS), are becoming more common in passenger vehicles, with the general aim of improving driver safety through automation of various aspects of the driving task. Drivers have generally reported satisfaction with ADAS with the exception of LDW systems, which are often rated poorly or even deactivated by drivers. One potential contributor to this negative response may be an increase in the cognitive load associated with lane-keeping when LDW is in use. The present study sought to examine the relationship between LDW, lane-keeping behavior, and concurrent cognitive load, as measured by performance on a secondary task. Participants drove a vehicle equipped with LDW in a demarcated lane on a closed-course test track with and without the LDW system in use over multiple sessions.
2017-03-28
Technical Paper
2017-01-0045
Guirong Zhuo, Cheng Wu, Fengbo Zhang
Abstract Vehicle active collision avoidance includes collision avoidance by braking and by steering. However, both of these two methods have their limitations. Therefore, it is significant to establish the feasible region of active collision avoidance to choose the optimal way to avoid traffic accidents. This paper focuses on the steering control of an autonomous vehicle to track the planned trajectory and to perform an emergency collision avoidance maneuver. Meanwhile, the collision avoidance effect of steering control is compared with that of braking control. The path tracking controller is designed by hierarchical control structure. The upper controller includes model predictive control allocation and speed controller, and the lower is designed by weighted least-squares control allocation for torque allocation. Besides, seven order polynomial is used for path planning.
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
2017-01-0116
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
2017-01-0032
Wei Yang, Ling Zheng, Yinong Li, Yue Ren, Yusheng Li
Abstract This paper proposed a two-section trajectory planning algorithm. In this trajectory planning, sigmoid function is adopted to fit two tangent arcs to meet limited parking spaces by reducing the radius of turning. Then the transverse preview model is established and the path tracking errors including distance error and angle error are estimated. The weight coefficient is considered to distribute the impact factor of traverse distance error or traverse angle error in the total error. The fuzzy controller is designed to track the two-section trajectory in autonomous intelligent parking system. The fuzzy controller is developed due to its real-time and robustness in the parking process. Traverse errors and its first-order derivative are selected as input variables and the outer wheel steering angle is selected as the output variable in fuzzy controller. They are also divided into seven fuzzy sets. Finally, forty rules are decided to achieve effective trajectory tracking.
2017-03-28
Technical Paper
2017-01-0041
Shengguang Xiong, Gangfeng Tan, Xuexun Guo, Longjie Xiao
Abstract Automotive Front Lighting System(AFS) can receive the steering signal and the vehicular speed signal to adjust the position of headlamps automatically. AFS will provide drivers more information of front road to protect drivers safe when driving at night. AFS works when there is a steering signal input. However, drivers often need the front road's information before they turn the steering wheel when vehicles are going to go through a sharp corner, AFS will not work in such a situation. This paper studied how to optimize the working time of AFS based on GIS (Geographic Information System) and GPS(Geographic Information System) to solve the problem. This paper analyzed the process of the vehicle is about to go through a corner. Low beams and high beams were discussed respectively.
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
2017-01-0113
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
Journal Article
2017-01-0418
Gregory McCann, Prashant Khapane
Abstract An increase in data measurement and recording within vehicles has allowed Anti-lock Braking Systems (ABS) to monitor a vehicle’s dynamic behavior in far more detail. This increased monitoring helps to improve vehicle response in scenarios such as braking whilst cornering and braking on uneven surfaces. The Durability and Robustness (D&R) CAE department within Jaguar Land Rover discovered that the lack of a complex ABS system in virtual vehicle models was contributing to poor lateral and longitudinal loads correlation throughout the suspension and mounting systems. D&R CAE started a project to incorporate Continental’s ABS system, provided by ‘©Continental AG’ for physical JLR vehicles, into SIMPACK virtual vehicles by means of a co-simulation (2017 n.d.). The work involved collaboration between 3 departments in Jaguar Land Rover and ultimately led to implementation of the ABS into the JLR standard automotive virtual database.
2017-03-28
Technical Paper
2017-01-0107
Arvind Jayaraman, Ashley Micks, Ethan Gross
Abstract Recreating traffic scenarios for testing autonomous driving in the real world requires significant time, resources and expense, and can present a safety risk if hazardous scenarios are tested. Using a 3D virtual environment to enable testing of many of these traffic scenarios on the desktop or cluster significantly reduces the amount of required road tests. In order to facilitate the development of perception and control algorithms for level 4 autonomy, a shared memory interface between MATLAB, Simulink, and Unreal Engine 4 can send information (such as vehicle control signals) back to the virtual environment. The shared memory interface conveys arbitrary numerical data, RGB image data, and point cloud data for the simulation of LiDAR sensors.
2017-03-28
Journal Article
2017-01-0118
Yang Wang, Ankit Goila, Rahul Shetty, Mahdi Heydari, Ambarish Desai, Hanlong Yang
Regarding safety, obstacle avoidance has been considered as one of the most important features among ADAS systems for ground vehicles. However, the implementation of obstacle avoidance functions to commercial vehicles are still under progress. In this paper, we demonstrate a complete process of obstacle avoidance strategy for unmanned ground vehicle and implement the strategy on the self-developed Arduino based RC Car. In this process, the sensor LIDAR was used to detect the obstacles on the fore-path. Based on the measured LIDAR data, an optimized path is automatically generated with accommodation of current car position, obstacle locations, car operation capability and global environmental restrictions. The path planning is updated in real time while new or changing obstacles being detected. This algorithm is validated by the simulation results with the RC car. The comparison will be discussed at the end of this paper.
2017-01-10
Technical Paper
2017-26-0007
Siva Murugesan, Vishakha S Bhagat, B V Shamsundara, Abhay Mannikar
Abstract In year 2015, 17 people were killed every hour by road accidents in India [1]. The occurrence of road accidents is observed to be higher during night, when visibility is at its lowest. The two factors which affect visibility are insufficient illumination and glare caused by the oncoming traffic. The Adaptive Front Lighting System [AFS] is an active safety feature which addresses these problems by employing specific lighting modes for Town, Country, Expressway conditions and automatic switching between Driving Beam and Passing Beam whenever required. Matrix of LEDs or a Projector with an actuator or a combination of both is employed in achieving different Lighting modes. The projector based AFS module is preferred for implementing the AFS control logic for passing beam owing to its economic cost.
2016-09-27
Journal Article
2016-01-8011
Kevin Grove, Jon Atwood, Myra Blanco, Andrew Krum, Richard Hanowski
Abstract This study evaluated the performance of heavy vehicle crash avoidance systems (CASs) by collecting naturalistic driving data from 150 truck tractors equipped with Meritor WABCO OnGuardTM or Bendix® Wingman® AdvancedTM products. These CASs provide drivers with audio-visual alerts of potential conflicts, and can apply automatic braking to mitigate or prevent a potential collision. Each truck tractor participated for up to one year between 2013 and 2015. Videos of the forward roadway and drivers’ faces were collected along with vehicle network data while drivers performed their normal duties on revenue-producing routes. The study evaluated the performance of CAS activations by classifying them into three categories based on whether a valid object was being tracked and whether drivers needed to react immediately.
Viewing 1 to 30 of 551