IU Indianapolis ScholarWorks :: Browsing by Author "Sherony, Rini"

Browsing by Author "Sherony, Rini"

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Certainty and Critical Speed for Decision Making in Tests of Pedestrian Automatic Emergency Braking Systems
(IEEE, 2016-09) Rosado, Alberto López; Chien, Stanley; Li, Lingxi; Yi, Qiang; Chen, Yaobin; Sherony, Rini; Department of Electrical and Computer Engineering, School of Engineering and Technology
This paper starts with depicting the test series carried out by the Transportation Active Safety Institute, with two cars equipped with pedestrian automatic emergency braking (AEB) systems. Then, an AEB analytical model that allows the prediction of the crash speed, stopping distance, and stopping time with a high degree of accuracy is presented. The model has been validated with the test results and can be used for real-time application due to its simplicity. The concept of the active safety margin is introduced and expressed in terms of deceleration, time, and distance in the model. This margin is a criterion that can be used either in the design phase of pedestrian AEB for real-time decision making or as a characteristic indicator in test procedures. Finally, the decision making is completed with the analysis of the behavior of the pedestrian lateral movement and the calculation of the certainty of finding the pedestrian into the crash zone. This model of certainty completes the analysis of decision making and leads to the introduction of the new concept of “critical speed for decision making.” All major variables influencing the performance of pedestrian AEB have been modeled. A proposal of certainty scale in this kind of tests and a set of recommendations are given to improve the efficiency and accuracy of evaluation of pedestrian AEB systems.
Contrast Between Road and Roadside Material For Road Edge Detection In Vehicle Road Departure Mitigation System
(National Highway Traffic Safety Administration, 2019) Yi, Qiang; Chien, Stanley; Chen, Yaobin; Sherony, Rini; Electrical and Computer Engineering, School of Engineering and Technology
Vehicle roadway departure crashes results in a large number of fatalities in the U.S. Road departure mitigation (RDM) systems rely on the road edge and road boundary identification. Cameras are widely used in RDMS for identifying road edges. The contrast between road and road boundary objects is one of the key image features used by the camera to detect road edges. This paper analyzes and compares the contrasts between various road surfaces. and road edges.
Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems
(IEEE, 2018) Shen, Dan; Yi, Qiang; Li, Lingxi; Chien, Stanley; Chen, Yaobin; Sherony, Rini; Mechanical and Energy Engineering, School of Engineering and Technology
Road departure detection systems (RDDSs) for avoiding/mitigating road departure crashes have been developed and included on some production vehicles in recent years. In order to support and provide a standardized and objective performance evaluation of RDDSs, this paper describes the development of the data acquisition and data post-processing systems for testing RDDSs. Seven parameters are used to describe road departure test scenarios. The overall structure and specific components of data collection system and data post-processing system for evaluating vehicle RDDSs is devised and presented. Experimental results showed sensing system and data post-processing system could capture all needed signals and display vehicle motion profile from the testing vehicle accurately. Test track testing under different scenarios demonstrates the effective operations of the proposed data collection system.
Determine characteristics requirement for the surrogate road edge objects for road departure mitigation testing
(2019) Chien, Stanley; Yi, Qiang; Lin, Jun; Saha, Abir; Li, Lin; Chen, Yaobin; Chen, Chi-Chih; Sherony, Rini; Electrical and Computer Engineering, School of Engineering and Technology
Road departure mitigation system (RDMS), a vehicle active safety feature, uses road edge objects to determine potential road departure. In the U.S., 45%, 16%, and 15% of car-mile (traffic flow * miles) roads have grass, metal guardrail, and concrete divider as road edge, respectively. It is difficult to test RDMS with real roadside objects. Lightweight and crashable surrogate roadside objects that have representative radar, LIDAR and camera characteristics of real objects have been developed for testing. This paper describes the identification of automotive radar, LIDAR, and visual characteristics of metal guardrail, concrete divider, and grass. These characteristics will be referenced for designing and fabricating the representative surrogate objects for RDMS testing. Colors and types of the roadside objects were identified from 24,735 randomly sampled locations in the US using Google street view images. The radar and LIDAR parameters were measured using 24GHz/77GHz radar and 350-2500nm IR spectrometer.
Development of Bicycle Surrogate for Bicyclist Pre-Collision System Evaluation
(SAE, 2016-04) Yi, Qiang; Chien, Stanley; Brink, Jason; Niu, Wensen; Li, Lingxi; Chen, Yaobin; Chen, Chi-Chen; Sherony, Rini; Takahashi, Hiroyuki; Department of Electrical and Computer Engineering, School of Engineering and Technology
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. The surrogate bicycle is constructed with detachable components with shatter resistant material to prevent structural damage during a collision, and matches the look and RCS of a real 26 inch mountain bicycle from all 360 degree angles. The surrogate bicycle rider is a 168 cm tall adult with CNC machined realistic body shape. The skin of the surrogate bicycle rider has the RCS of a real human skin. Combined skin with realistic body shape, the surrogate bicyclist has the RCS matching to that of a same sized real human from 360 degree angles in the view of 77GHz automotive radar. The surrogate bicyclist has articulated leg motion which is important for micro Doppler sensing and can be supported on a sled or a mobile carrier. It can be moved at a speed of 20 mph and can be collided by vehicles from any direction and be reassembled in less than 5 minutes.
Development of Surrogate Grass for the Evaluation of Vehicle Road Departure Mitigation Systems
(IEEE, 2020-09) Chien, Stanley; Zhou, Jue; Yi, Qiang; Pandey, Seeta Ram; Saha, Abir; Lin, Jun; Chen, Yaobin; Sherony, Rini; Electrical and Computer Engineering, School of Engineering and Technology
Vehicle road departure mitigation system (RDMS), as new active safety technology, has been introduced into the market in recent years. This system can detect roadside objects and road edges to reduce the risk of roadway departure crashes. To evaluate and improve the performance of RDMS, surrogates of roadside objects, which have the same camera, radar, and LiDAR characteristics of the real objects, need to be developed. Grass is the most common road edge in the U.S. as seen from the real road data. This paper describes the development of surrogate grass. The LiDAR (infrared) and radar characteristics of the selected artificial turf (grass) are obtained and compared with those of real grass. In order to make the surrogate grass match the real grass in the view of sensors (LiDAR, radar and camera), a special color coating with high reflectance material is applied to the artificial turf. Both LiDAR and radar measurements confirmed that the surrogate grass closely match the key characteristics of the real grass. Five grass colors and eighteen color patterns were identified based on 1,021 grass road-edge samples from all states of the U.S. 300-meter long surrogate grass was made and successfully used on the test track for the vehicle RDMS evaluation.
An Extreme Learning Machine-based Pedestrian Detection Method
(Office of the Vice Chancellor for Research, 2013-04-05) Yang, Kai; Du, Eliza Y.; Delp, Edward J.; Jiang, Pingge; Jiang, Feng; Chen, Yaobin; Sherony, Rini; Takahashi, Hiroyuki
Pedestrian detection is a challenging task due to the high variance of pedestrians and fast changing background, especially for a single in-car camera system. Traditional HOG+SVM methods have two challenges: (1) false positives and (2) processing speed. In this paper, a new pedestrian detection method using multimodal HOG for pedestrian feature extraction and kernel based Extreme Learning Machine (ELM) for classification is presented. The experimental results using our naturalistic driving dataset show that the proposed method outperforms the traditional HOG+SVM method in both recognition accuracy and processing speed.
Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development
(SAE, 2016-04) Sherony, Rini; Tian, Renran; Chien, Stanley; Fu, Li; Chen, Yaobin; Takahashi, Hiroyuki; Department of Engineering Technology, School of Engineering and Technology
Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions. We modeled the pedestrian/bicyclist limb motions in four layers: (1) the percentages of pedestrians and bicyclists who have limb motions when crossing the road; (2) the averaged action frequency and the corresponding distributions on when there are limb motions; (3) comparisons of the limb motion behavior between crossing and non-crossing cases; and (4) the effects of seasons on the limb motions when the pedestrians/bicyclists are crossing the road. The results of this study can provide empirical foundations supporting surrogate development, benefit analysis, and standardized testing of vehicular pedestrian/bicyclist detection and crash mitigation systems.
SceNDD: A Scenario-based Naturalistic Driving Dataset
(IEEE, 2022-10) Prabu, Avinash; Ranjan, Nitya; Li, Lingxi; Tian, Renran; Chien, Stanley; Chen, Yaobin; Sherony, Rini; Electrical and Computer Engineering, School of Engineering and Technology
In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20–40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by the end of 2022.

Browsing by Author "Sherony, Rini"

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