Browsing by Subject "autonomous vehicles"

Now showing 1 - 3 of 3
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    Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)
    (IEEE, 2019-09) Katare, Dewant; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    An Autonomous vehicle or present day smart vehicle is equipped with several ADAS safety features such as Blind Spot Detection, Forward Collision Warning, Lane Departure and Parking Assistance, Surround View System, Vehicular communication System. Recent research utilize deep learning algorithms as a counterfeit for these traditional methods, using optimal sensors. This paper discusses the perception tasks related to autonomous vehicle, specifically the computer-vision approach of 3D object detection and thus proposes a model compatible with embedded system using the RTMaps framework. The proposed model is based on the sensors: camera and Lidar connected to an autonomous embedded system, providing the sensed inputs to the deep learning classifier which on the basis of theses inputs estimates the position and predicts a 3-d bounding box on the physical objects. The Frustum PointNet a contemporary architecture for 3-D object detection is used as base model and is implemented with extended functionality. The architecture is trained and tested on the KITTI dataset and is discussed with the competitive validation precision and accuracy. The Presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.
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    Forward Collision Prediction with Online Visual Tracking
    (IEEE, 2019-09) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Safety is the key aspect when comes to driving. Self-driving vehicles are equipped with driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collsion Mitigation by Breaking (CMbB) to ensure safety while driving. This paper proposes a method by following a lean way of multi-target tracking implementation and 3D bounding box detection without processing much visual information. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object’s trajectories to accomplish motion planning. The advancement in the object detection methods greatly benefits when following the tracking by detection approach. This will lead to less complex tracking methodology and thus decreasing the computational cost. Estimation based on particle filter is added to precisely associate the tracklets with detections. The model estimates and plots bounding box for the objects in its camera range and predict the 3D positions in camera coordinates from monocular camera data using a deep learning combined with geometric constraints using 2D bounding box, then the actual distance from the vehicle camera is calculated. The model is evaluated on the KITTI car dataset.
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    A Multi Sensor Real-time Tracking with LiDAR and Camera
    (IEEE, 2020-01) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Self driving cars are equipped with various driver-assistive technologies (ADAS) like Forward Collision Warning system (FCW), Adaptive Cruise Control and Collision Mitigation by Breaking (CMbB) to ensure safety. Tracking plays an important role in ADAS systems for understanding dynamic environment. This paper proposes 3D multi-target tracking method by following a lean way of implementation using object detection with aim of real time. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object's trajectories to accomplish motion planning. The advancement in the object detection methodologies benefits greatly when following the tracking by detection approach. The proposed method implemented 2D tracking on camera data and 3D tracking on LiDAR point cloud data. The estimated state from each sensors are fused together to come with a more optimal state of objects present in the surrounding. The multi object tracking performance has evaluated on publicly available KITTI dataset.