Sensor Fusion for Object Detection and Tracking in Autonomous Vehicles

Autonomous driving vehicles depend on their perception system to understand the environment and identify all static and dynamic obstacles surrounding the vehicle. The perception system in an autonomous vehicle uses the sensory data obtained from different sensor modalities to understand the environment and perform a variety of tasks such as object detection and object tracking. Combining the outputs of different sensors to obtain a more reliable and robust outcome is called sensor fusion. This dissertation studies the problem of sensor fusion for object detection and object tracking in autonomous driving vehicles and explores different approaches for utilizing deep neural networks to accurately and efficiently fuse sensory data from different sensing modalities. In particular, this dissertation focuses on fusing radar and camera data for 2D and 3D object detection and object tracking tasks. First, the effectiveness of radar and camera fusion for 2D object detection is investigated by introducing a radar region proposal algorithm for generating object proposals in a two-stage object detection network. The evaluation results show significant improvement in speed and accuracy compared to a vision-based proposal generation method. Next, radar and camera fusion is used for the task of joint object detection and depth estimation where the radar data is used in conjunction with image features to generate object proposals, but also provides accurate depth estimation for the detected objects in the scene. A fusion algorithm is also proposed for 3D object detection where where the depth and velocity data obtained from the radar is fused with the camera images to detect objects in 3D and also accurately estimate their velocities without requiring any temporal information. Finally, radar and camera sensor fusion is used for 3D multi-object tracking by introducing an end-to-end trainable and online network capable of tracking objects in real-time

Virtual screening based on the structure of more than 105 compounds against four key proteins of SARS-CoV-2: MPro, SRBD, RdRp, and PLpro

Background: SARS-CoV-2 initially originated in Wuhan (China) around December 2019, and spread all over the world. Currently, WHO (Word Health Organization) has licensed several vaccines for this viral infection. However, not everyone can be vaccinated. People with underlying health conditions that weaken their immune systems or those with severe allergies to some vaccine components, may not be able to be vaccinated. Moreover, no vaccination is 100% safe, and the emergence of new SARS-CoV-2 mutations may reduce the efficacy of immunizations. Therefore, it is urgent to develop effective drugs to protect people against this virus. Material and method: We performed structure-based virtual screening (SBVS) of a library that was built from ChemDiv and PubChem databases against four SARS‐CoV‐2 target proteins: S‐protein (spike), main protease (MPro), RNA-dependent RNA polymerase, and PLpro. A virtual screening study was performed using PyRx and AutoDock tools. Results: Our results suggest that twenty-five top-ranked drugs with the highest energy binding as the potential inhibitors against four SARS-CoV-2 targets, relative to the reference molecules. Based on the energy binding, we suggest that these compounds could be used to produce effective anti-viral drugs against SARS-CoV-2. Conclusion: The discovery of novel compounds for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins of the virus. These compounds should be further assessed in experimental assays and clinical trials to validate their actual activity against the disease. These findings may contribute to the drug design studies against COVID‐19