Automotive Ethernet as an in-vehicle networking paradigm has gradually become the main automotive backbone network. However, with the ever-increasing time-critical in-vehicle traffic, it is being confronted with enormous challenges to realize deterministic-latency communications, due to the inherent limitations of its distributed network architecture and the adopted MAC protocols, including limited-computing power, low-speed and unreliable traffic transmission. Furthermore, it is often incompatible with emerging vehicular functions and protocols, which can provide tremendous potential for better vehicular Quality-of-Service (QoS). Therefore, in this paper, we first design a Future Automotive Ethernet (FAE) architecture and then, built on the representative Time-Sensitive Networking (TSN) and industrial summation frame, propose a Deterministic-Latency MAC (DLM) protocol running in FAE to tackle these issues. The FAE architecture includes three functional domains, three in-vehicle computing units, and no fewer than three intelligent network processing modules, which can integrate with the emerging LAN protocols to realize cross-domain and intra-domain Ethernet-based communications. Under the umbrella of this architecture, DLM classifies the driving situations into driving, reversing, left turn, right turn and parking states, following the real-world vehicle behaviors, and assigns all traffic associated with driving safety in five states different recommended priorities to be delivered. Furthermore, an optimized deterministic-latency mechanism integrated with TSN and the industrial summation frame is developed to realize the timely and accurate transmission of high-priority and medium/low-priority traffic. Simulation results obtained from diversified scenarios demonstrate that the proposed DLM running in FAE can significantly improve transmission latency determinacy and reliability compared with the existing technical strategies.

Waypoint estimation (WE) has a wide range of applications for indoor walkers, such as fire rescue and navigation to find exit doors, lifts, or stairs as examples of waypoints, etc. Data-driven WE has been on the rise with advancements in deep learning algorithms. The current WE methods, however, face two challenges. On the one hand, most waypoint detection approaches rely on visual sensors, hence, their estimation performance is limited by light when collecting visual data. On the other hand, data-driven methods necessitate a large number of labeled data to train a WE model, which significantly increases the time spent manually marking labels. Targeting the above two challenges, our work first proposes a novel deep Bayesian active learning waypoint estimator for indoor walkers (DeepWE) based on human activity recognition (HAR). This estimates six indoor waypoints through walkers’ daily activities due to the strong correlation between human activities and waypoints. First, an initial DeepWE model is developed using a Bayesian ensembled convolutional neural network (B-CNN) using the accelerometer and gyroscope data. Then, active learning is employed to query the most formative samples from pool points with four acquisition functions, and only these queried samples are labeled manually. Finally, the initial DeepWE model is updated from this labeled data using an incremental learning algorithm. Empirical results on two publicly available USC-HAD and OPPORTUNITY data sets show DeepWE performs a considerable accuracy boost for WE, with a substantial amount of acquired pool points reduction (more than 40%).

The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc fine-timing measurement (FTM) scheme also known as the Wi-Fi round trip time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of the following: 1) systematic bias determination and removal; 2) clustering-based trilateration (CbT) supported by weighted concentric circle generation (WCCG), namely CbT & WCCG; 3) positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & WCCG methods are compared with least square (LS) and recursive least square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer–Rao bound (CRB).

Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities.