Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial Intelligence. Despite the many advantages and opportunities of these systems within various application domains, the scarcity of energy, extensive computing needs, and limited communication must be considered when orchestrating their deployment. Inducing savings in these directions is central to the Approximate Computing (AxC) paradigm, in which the accuracy of some operations is traded off with energy, latency, and/or communication reductions. Unfortunately, the dynamics of the environments in which AxC-equipped IoT systems operate have been paid little attention. We bridge this gap by surveying adaptive AxC techniques applied to three emerging application domains, namely autonomous driving, smart sensing and wearables, and positioning, paying special attention to hardware acceleration. We discuss the challenges of such applications, how adaptive AxC can aid their deployment, and which savings it can bring based on traits of the data and devices involved. Insights arising thereof may serve as inspiration to researchers, engineers, and students active within the considered domains.

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In recent years, there has been a notable increase in the size of commonly used image classification models. This growth has empowered models to recognize thousands of diverse object types. However, their computational demands pose significant challenges, especially when deploying them on resource-constrained edge devices. In many use cases where a model is deployed on an edge device, only a small subset of the classes will ever be observed by a given model instance. Our proposed test-time specialization of dynamic neural networks allows these models to become faster at recognizing the classes that are observed frequently, while maintaining the ability to recognize all other classes, albeit slightly less efficient. We benchmark our approach on a real-world edge device, obtaining significant speedups compared to the baseline model without test-time adaptation.

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Connected vehicular services depend heavily on communication as they frequently transmit data and AI models/weights within the vehicular ecosystem. Energy efficiency in vehicles is crucial to keep up with the fast-growing demand for vehicular data processing and communication. To tackle this rising challenge, we explore approximation and edge AI techniques for achieving energy efficiency for vehicular services. Focusing on data-intensive vehicular services, we present an experimental case study on the high-definition (HD) map using the model partition approach. Our study compares the AI model energy consumption using multiple approximation ratios over embedded edge devices. Based on experimental insights, we further discuss an envisioned approximate Edge AI pipeline for developing and deploying energy-efficient vehicular services.@en

A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.@en

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.@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

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

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

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