Display:

Results

Viewing 1 to 30 of 565
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
2017-01-1964
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
2017-01-1978
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
2017-01-1998
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
2017-01-1958
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.
CURRENT
2017-08-24
Standard
AS8017D
This SAE Aerospace Standard (AS) establishes minimum performance standards for new equipment anticollision light systems.
2017-04-06
Magazine
Connectivity continues its advance More OEMs and Tier 1 suppliers are focusing on embedded telematic systems, hoping to displace aftermarket hardware. Tailoring fuel injection to control NOx The next big step to help heavy-duty diesel engines meet stricter emissions regulations involves adapting the fuel-injection system to the combustion needs. Active on safety Crash-avoidance technologies are vital "building blocks" to automate commercial vehicles, implement truck platooning and ultimately achieve zero accidents. Engineering with simulation and data Companies are discovering new simulation techniques, especially optimization; the next step is to combine simulation with sensor data and predictive analytics to create even more robust off-highway equipment.
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
2017-01-1399
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
Collection
Papers in the collection focus on Advanced Driver Assistance Systems (ADAS) which are gaining major importance all vehicle segments. The effectiveness of these systems is based upon the ability to not only sense the outside world and the ability to use the information intelligently.
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
2017-01-0032
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
2017-01-0118
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.
CURRENT
2017-01-26
Standard
J2808_201701
The Lane Departure Warning (LDW) system is a crash-avoidance technology which warns drivers if they are drifting (or have drifted) out of their lane or from the roadway. This warning system is designed to reduce the possibility of a run-off-road crash. This system will not take control of the vehicle; it will only let the driver know that he/she needs to steer back into the lane. An LDW is not a lane-change monitor, which addresses intentional lane changes, or a blind spot monitoring system which warns of other vehicles in adjacent lanes. This informational report applies to OEM and after-market Lane Departure Warning systems for light-duty vehicles (gross vehicle weight rating of no more than 8500 pounds) on relatively straight roads with a radius of curvature of 500 m or more, and under good weather conditions.
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.
2016-09-20
Journal Article
2016-01-1976
Kiran Thupakula, Adishesha Sivaramasastry, Srikanth Gampa
Abstract Aviation safety is one of the key focus areas of the aerospace industry as it involves safety of passengers, crew, assets etc. Due to advancements in technology, aviation safety has reached to safest levels compared to last few decades. In spite of declining trends in in-air accident rate, ground accidents are increasing due to ever increasing air traffic and human factors in the airport. Majority of the accidents occur during initial and final phases of the flight. Rapid increase in air traffic would pose challenge in ensuring safety and best utilization of Airports, Airspace and assets. In current scenario multiple systems like Runway Debris Monitoring System, Runway Incursion Detection System, Obstacle avoidance system and Traffic Collision Avoidance System are used for collision prediction and alerting in airport environment. However these approaches are standalone in nature and have limitations in coverage, performance and are dependent on onboard equipment.
2016-04-05
Technical Paper
2016-01-1446
Rini Sherony, Qiang Yi, Stanley Chien, Jason Brink, Mohammad Almutairi, Keyu Ruan, Wensen Niu, Lingxi Li, Yaobin Chen, Hiroyuki Takahashi
Abstract According to the U.S. National Highway Traffic Safety Administration, 743 pedal cyclists were killed and 48,000 were injured in motor vehicle crashes in 2013. As a novel active safety equipment to mitigate bicyclist crashes, bicyclist Pre-Collision Systems (PCSs) are being developed by many vehicle manufacturers. Therefore, developing equipment for evaluating bicyclist PCS is essential. This paper describes the development of a bicycle carrier for carrying the surrogate bicyclist in bicyclist PCS testing. An analysis on the United States national crash databases and videos from TASI 110 car naturalistic driving database was conducted to determine a set of most common crash scenarios, the motion speed and profile of bicycles. The bicycle carrier was designed to carry or pull the surrogate bicyclist for bicycle PCS evaluation. The carrier is a platform with a 4 wheel differential driving system.
2016-04-05
Technical Paper
2016-01-1453
I-Hsuan Lee, Bi-Cheng Luan
Abstract Autonomous emergency braking (AEB) systems is one of the functions of the Advanced Driver Assists System to avoid or mitigate vehicle frontal collisions. Most of the previous studies focus on two-car scenario where the host vehicle monitors the distances to the vehicles in front, and automatically applies emergency brake when a collision is imminent. The purpose of this paper is to develop an Advanced-AEB control system that mitigates collisions in a multi-car scenario by measuring the distances to the vehicles in front as well as those to the vehicles behind using the concept of impedance control. A simple gain-scheduling PI controller was designed for the host vehicle to track the reference inputs generated by the impedance control. The preliminary simulation results demonstrate that the proposed AEB is effective in mitigating the collisions in a 3-car following scenario.
2016-04-05
Technical Paper
2016-01-1454
Libo Dong, Stanley Chien, Jiang-Yu Zheng, Yaobin Chen, Rini Sherony, Hiroyuki Takahashi
Abstract Pedestrian Automatic Emergency Braking (PAEB) for helping avoiding/mitigating pedestrian crashes has been equipped on some passenger vehicles. Since approximately 70% pedestrian crashes occur in dark conditions, one of the important components in the PAEB evaluation is the development of standard testing at night. The test facility should include representative low-illuminance environment to enable the examination of the sensing and control functions of different PAEB systems. The goal of this research is to characterize and model light source distributions and variations in the low-illuminance environment and determine possible ways to reconstruct such an environment for PAEB evaluation. This paper describes a general method to collect light sources and illuminance information by processing large amount of potential collision locations at night from naturalistic driving video data.
2016-04-05
Technical Paper
2016-01-1447
Qiang Yi, Stanley Chien, Jason Brink, Wensen Niu, Lingxi Li, Yaobin Chen, Chi-Chen Chen, Rini Sherony, Hiroyuki Takahashi
Abstract As part of active safety systems for reducing bicyclist fatalities and injuries, Bicyclist Pre-Collision System (BPCS), also known as Bicyclist Autonomous Emergency Braking System, is being studied currently by several vehicles manufactures. This paper describes the development of a surrogate bicyclist which includes a surrogate bicycle and a surrogate bicycle rider to support the development and evaluation of BPCS. The surrogate bicycle is designed to represent the visual and radar characteristics of real bicyclists in the United States. The size of bicycle surrogate mimics the 26 inch adult bicycle, which is the most popular adult bicycle sold in the US. The radar cross section (RCS) of the surrogate bicycle is designed based on RCS measurement of the real adult sized bicycles.
2016-04-05
Journal Article
2016-01-1449
Taylor Johnson, Rong Chen, Rini Sherony, Hampton C. Gabler
Abstract Lane departure warning (LDW) systems can detect an impending road departure and deliver an alert to allow the driver to steer back to the lane. LDW has great potential to reduce the number of road departure crashes, but the effectiveness is highly dependent upon driver acceptance. If the driver perceives there is little danger after receiving an alert, the driver may become annoyed and deactivate the system. Most current LDW systems rely heavily upon distance to lane boundary (DTLB) in the decision to deliver an alert. There is early evidence that in normal driving DTLB may be only one of a host of other cues which drivers use in lane keeping and in their perception of lane departure risk. A more effective threshold for LDW could potentially be delivered if there was a better understanding of this normal lane keeping behavior. The objective of this paper is to investigate the lane keeping behavior of drivers in normal driving.
Viewing 1 to 30 of 565

Filter