IU Indianapolis ScholarWorks :: Browsing by Author "Chen, Yaobin"

Browsing by Author "Chen, Yaobin"

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Acoustic Simultaneous Localization And Mapping (SLAM)
(2021-12) Madan, Akul; Li, Lingxi; Chen, Yaobin; King, Brian
The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.
An Adaptive Eye Gaze Tracking System Without Calibration for Use in an Automobile
(2011) Rajabather, Harikrishna K.; Koskie, Sarah; Chen, Yaobin; Christopher, Lauren
One of the biggest hurdles to the development of an effective driver state monitor is the that there is no real-time eye-gaze detection. This is primarily due to the fact that such systems require calibration. In this thesis the various aspects that comprise an eye gaze tracker are investigated. From that we developed an eye gaze tracker for automobiles that does not require calibration. We used a monocular camera system with IR light sources placed in each of the three mirrors. The camera system created the bright-pupil effect for robust pupil detection and tracking. We developed an SVM based algorithm for initial eye candidate detection; after that the eyes were tracked using a hybrid Kalman/Mean-shift algorithm. From the tracked pupils, various features such as the location of the glints (reflections in the pupil from the IR light sources) were extracted. This information is then fed into a Generalized Regression Neural Network (GRNN). The GRNN then maps this information into one of thirteen gaze regions in the vehicle.
Analysis of Potential Co-Benefits for Bicyclist Crash Imminent Braking Systems
(IEEE, 2017-10) Good, David H.; Krutilla, Kerry; Chien, Stanley; Li, Lingxi; Chen, Yaobin; Electrical and Computer Engineering, School of Engineering and Technology
In the US, the number of traffic fatalities has had a long term downward trend as a result of advances in the crash worthiness of vehicles. However, these improvements in crash worthiness do little to protect other vulnerable road users such as pedestrians or bicyclists. Several manufacturers have developed a new generation of crash avoidance systems that attempt to recognize and mitigate imminent crashes with non-motorists. While the focus of these systems has been on pedestrians where they can make meaningful contributions to improved safety [1], recent designs of these systems have recognized mitigating bicyclist crashes as a potential co-benefit. This paper evaluates the performance of one system that is currently available for consumer purchase. Because the vehicle manufacturer does not claim effectiveness for their system under all crash geometries, we focus our attention on the crash scenario that has the highest social cost in the US: the cyclist and vehicle on parallel paths being struck from behind. Our analysis of co benefits examines the ability to reduce three measures: number of crashes, fatalities, and a comprehensive measure for social cost that incorporates morbidity and mortality. Test track simulations under realistic circumstances with a realistic surrogate bicyclist target are conducted. Empirical models are developed for system performance and potential benefits for injury and fatality reduction. These models identify three key variables in the analysis: vehicle speed, cyclist speed and cyclist age as key determinants of potential co-benefits. We find that the evaluated system offers only limited benefits for any but the oldest bicycle riders for our tested scenario.
Assessing the Effectiveness of In-Vehicle Highway Back-of-Queue Alerting System
(The National Academies of Sciences, Engineering, and Medicine, 2021-01) Shen, Dan; Zhang, Zhengming; Ruan, Keyu; Tian, Renran; Li, Lingxi; Li, Feng; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
This paper proposes an in-vehicle back-of-queue alerting system that is able to issue alerting messages to drivers on highways approaching traffic queues. A prototype system was implemented to deliver the in-vehicle alerting messages to drivers via an Android-based smartphone app. To assess its effectiveness, a set of test scenarios were designed and implemented on a state-of-the-art driving simulator. Subjects were recruited and their testing data was collected under two driver states (normal and distracted) and three alert types (no alerts, roadside alerts, and in-vehicle auditory alerts). The effectiveness was evaluated using three parameters of interest: 1) the minimum Time-to-Collision (mTTC), 2) the maximum deceleration, and 3) the maximum lateral acceleration. Statistical models were utilized to examine the usefulness and benefits of each alerting type. The results show that the in-vehicle auditory alert is the most effective way for delivering alerting messages to drivers. More specifically, it significantly increases the mTTC (30% longer than that of 'no warning') and decreases the maximum lateral acceleration (60% less than that of 'no warning'), which provides drivers with more reaction time and improves driving stability of their vehicles. The effects of driver distraction significantly decrease the efficiency of roadside traffic sign alert. More specifically, when the driver is distracted, the roadside traffic sign alert performs significantly worse in terms of mTTC compared with that of normal driving. This highlights the importance of the in-vehicle auditory alert when the driver is distracted.
Automatic Modeling and Simulation of Networked Components
(2011) Bruce, Nathaniel William; Koskie, Sarah; Chen, Yaobin; Li, Lingxi
Testing and verification are essential to safe and consistent products. Simulation is a widely accepted method used for verification and testing of distributed components. Generally, one of the major hurdles in using simulation is the development of detailed and accurate models. Since there are time constraints on projects, fast and effective methods of simulation model creation emerge as essential for testing. This thesis proposes to solve these issues by presenting a method to automatically generate a simulation model and run a random walk simulation using that model. The method is automated so that a modeler spends as little time as possible creating a simulation model and the errors normally associated with manual modeling are eliminated. The simulation is automated to allow a human to focus attention on the device that should be tested. The communications transactions between two nodes on a network are recorded as a trace file. This trace file is used to automatically generate a finite state machine model. The model can be adjusted by a designer to add missing information and then simulated in real-time using a software-in-the-loop approach. The innovations in this thesis include adaptation of a synthesis method for use in simulation, introduction of a random simulation method, and introduction of a practical evaluation method for two finite state machines. Test results indicate that nodes can be adequately replaced by models generated automatically by these methods. In addition, model construction time is reduced when comparing to the from scratch model creation method.
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.
Collision Avoidance for Automated Vehicles Using Occupancy Grid Map and Belief Theory
(2021-08) Soltani, Reza; Li, Lingxi; Koskie, Sarah; Chen, Yaobin
This thesis discusses occupancy grid map, collision avoidance system and belief theory, and propose some of the latest and the most effective method such as predictive occupancy grid map, risk evaluation model and OGM role in the belief function theory with the approach of decision uncertainty according to the environment perception with the degree of belief in the driving command acceptability. Finally, how the proposed models mitigate or prevent the occurrence of the collision.
Collision-Free Path Planning for Automated Vehicles Risk Assessment via Predictive Occupancy Map
(IEEE, 2020-11) Shen, Dan; Chen, Yaobin; Li, Lingxi; Chien, Stanley; Electrical and Computer Engineering, School of Engineering and Technology
Vehicle collision avoidance system (CAS) is a control system that can guide the vehicle into a collision-free safe region in the presence of other objects on road. Common CAS functions, such as forward-collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, these CASs focus on mitigating or avoiding potential crashes with the preceding cars and objects. They are not effective for crash scenarios with vehicles from the rear-end or lateral directions. This paper proposes a novel collision avoidance system that will provide the vehicle with all-around (360-degree) collision avoidance capability. A risk evaluation model is developed to calculate potential risk levels by considering surrounding vehicles (according to their relative positions, velocities, and accelerations) and using a predictive occupancy map (POM). By using the POM, the safest path with the minimum risk values is chosen from 12 acceleration-based trajectory directions. The global optimal trajectory is then planned using the optimal rapidly exploring random tree (RRT*) algorithm. The planned vehicle motion profile is generated as the reference for future control. Simulation results show that the developed POM-based CAS demonstrates effective operations to mitigate the potential crashes in both lateral and rear-end crash scenarios.
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.
Crash Prediction and Collision Avoidance using Hidden Markov Model
(2019-08) Prabu, Avinash; Li, Lingxi; King, Brian; Chen, Yaobin
Automotive technology has grown from strength to strength in the recent years. The main focus of research in the near past and the immediate future are autonomous vehicles. Autonomous vehicles range from level 1 to level 5, depending on the percentage of machine intervention while driving. To make a smooth transition from human driving and machine intervention, the prediction of human driving behavior is critical. This thesis is a subset of driving behavior prediction. The objective of this thesis is to predict the possibility of crash and implement an appropriate active safety system to prevent the same. The prediction of crash requires data of transition between lanes, and speed ranges. This is achieved through a variation of hidden Markov model. With the crash prediction and analysis of the Markov models, the required ADAS system is activated. The above concept is divided into sections and an algorithm was developed. The algorithm is then scripted into MATLAB for simulation. The results of the simulation is recorded and analyzed to prove the idea.

Browsing by Author "Chen, Yaobin"

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