Department Publications

Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving …

Dynamic adjustment of signal timing based on the number of pedestrians waiting at a crossing results in safer and more efficient intersections. Traditionally, such information is obtained through push-buttons or vision-based techniques. Though counting is possible with the latter, the techniques are associated with privacy concerns, are affected by poor lighting, and equipment is costly and bulky. We present a novel alternative that uses Wi-Fi Channel State Information (CSI) processed through a Deep Neural Network (DNN) model. The amplitude and phase of 51 Orthogonal Frequency Division Multiplexing sub carriers in the IEEE802.11n standard are captured with two ESP32 devices. Of the candidate DNNs trained with the CSI data, a ID-Convolutional Neural Network is experimentally demonstrated to count up to 12 pedestrians with an accuracy of 79 % at the current level of training. This can be generalized to …

Supporting parallel multi-priority data streams is vital for maintaining the quality of service (QoS) in vehicle-to-everything (V2X) communication. Hence, the European telecommunications standard institute (ETSI) has defined four packet types, with varying priority levels, to be used as broadcast packets in such communication. This paper studies the medium access control (MAC) layer performance of IEEE 802.11p and cellular-V2X (C-V2X) Mode 4 using discrete-time Markov chain (DTMC) based models, while considering parallel multi-priority data streams. The overall model consists of four queue models with their respective traffic generators, which are appropriately linked with the DTMCs modeling the MAC layer operations of IEEE 802.11p and C-V2X Mode 4. Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance …