An Autonomous vehicle depends on the combination of latest technology or ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning/Avoidance (FCW or FCA). The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. The concept behind a FCW algorithm is the measure of a distance or warning range which results in an alert to notify or inform the driver regarding the possible collision. The objective of this paper is to propose a collision warning model using the RTMaps framework. The proposed model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity, and separation distance to a neural network based classifier algorithm which on the basis of supervised learning alerts the driver of a possible collision. The implementation, precision and accuracy of the classifier and regression algorithm is discussed and compared. The presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.@en

Present day autonomous vehicle relies on several sensor technologies for it's autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line of sight sensors and are responsible for the different level of autonomy. These line of sight sensors are used for the execution of actions related to localization, object detection and the complete environment understanding. The surrounding or environment understanding for an autonomous vehicle can be achieved by segmentation. Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available, however with the advancement in the computing processor, the progression is on developing the deep learning application replacing traditional methods. This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose. The proposed model for outdoor scene segmentation is based on the frustum pointnet, and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level. For real time application the model is deployed on the RTMaps framework with Bluebox (an embedded platform for autonomous vehicle). The proposed architecture is trained with the CITYScpaes and the KITTI dataset.@en

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.@en

Forward Collision Warning (FCW) also referred as the Forward Collision Avoidance (FCA) system has become an essential part of the Autonomous cars or smart vehicles. The FCW System has found its position as one of the foremost automobile safety features included in the present smart vehicles. This paper proposes a Forward collision warning system which includes its subsystem as Forward-looking automotive radar model and a Classifier algorithm developed and designed in the Intempora RTMaps Embedded with NXP Bluebox 2.0. The classifier and regression algorithm work on the principle of supervised learning, the proposed model predicts the collision or no-collision occurrence based on the radar sensed inputs. The implementation, precision and accuracy of the classifier and regression algorithm is presented. The proposed model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.@en

Internet of Things (IoT) is currently an emerging trend in the industry and is proving to be the driving force in the field of research and development. Due to a growing demand in industries for a cloud-based solution and easy integration with existing hardware devices, IoT has become a popular choice among all verticals of industry: Engineering, Manufacturing, Accounts, Supply Chain and many more. This paper was a result of a project whose requirement was realtime detection for liquid levels in a chemical plants using a wireless GoPro Camera. The most obvious choice was OpenCV, and this would have been the most common solution. But a complete IOT based solution is proposed that would not only detect level but will also provide additional features like cloud storage, alert messages and Convolutional Neural Network (CNN) based level corrections @en