Browsing by Subject "Autonomous Vehicles"

Now showing 1 - 3 of 3
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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.
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    Improving Object Detection using Enhanced EfficientNet Architecture
    (2023-08) Kamel Ibrahim, Michael; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    EfficientNet is designed to achieve top accuracy while utilizing fewer parameters, in addition to less computational resources compared to previous models. In this paper, we are presenting compound scaling method that re-weight the network’s width (w), depth(d), and resolution (r), which leads to better performance than traditional methods that scale only one or two of these dimensions by adjusting the hyperparameters of the model. Additionally, we are presenting an enhanced EfficientNet Backbone architecture. We show that EfficientNet achieves top accuracy on the ImageNet dataset, while being up to 8.4x smaller and up to 6.1x faster than previous top performing models. The effec- tiveness demonstrated in EfficientNet on transfer learning and object detection tasks, where it achieves higher accuracy with fewer parameters and less computation. Henceforward, the proposed enhanced architecture will be discussed in detail and compared to the original architecture. Our approach provides a scalable and efficient solution for both academic research and practical applications, where resource constraints are often a limiting factor.
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    Intersection Collision Avoidance For Autonomous Vehicles Using Petri Nets
    (2019-08) Shankar Kumar, Valli Sanghami; Li, Lingxi; Chen, Yaobin; King, Brian
    Autonomous vehicles currently dominate the automobile field for their impact on humanity and society. Connected and Automated Vehicles (CAV’s) are vehicles that use different communication technologies to communicate with other vehicles, infrastructure, the cloud, etc. With the information received from the sensors present, the vehicles analyze and take necessary steps for smooth, collision-free driving. This the sis talks about the cruise control system along with the intersection collision avoidance system based on Petri net models. It consists of two internal controllers for velocity and distance control, respectively, and three external ones for collision avoidance. Fault-tolerant redundant controllers are designed to keep these three controllers in check. The model is built using a PN toolbox and tested for various scenarios. The model is also validated, and its distinct properties are analyzed.