Nowadays, and more than a decade after the first steps towards autonomous driving, we keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being a key instrument for the success of this technology. Such advances trigger the emergence of new players in the automotive industry, and along with car manufacturers, this sector represents a multibillion-dollar market where everyone wants to take a share. To understand recent advances and technologies behind LiDAR, this article presents a survey on LiDAR sensors for the automotive industry. With this work, we show the measurement principles and imaging techniques currently being used, going through a review of commercial systems and development solutions available in the market today. Furthermore, we highlight the current and future challenges, providing insights on how both research and industry can step towards better LiDAR solutions.

With the advent of the Internet of Things (IoT), security has emerged as a major design goal for smart connected devices. This explosion in connectivity created a larger attack surface area. Software-based approaches have been applied for security purposes; however, these methods must be extended with security-oriented technologies that promote hardware as the root of trust. The ARM TrustZone can enable trusted execution environments (TEEs), but existing solutions disregard real-time needs. Here, the authors demonstrate why TrustZone is becoming a reference technology for securing IoT edge devices, and how enhanced TEEs can help meet industrial IoT applications real-time requirements.

In a world with an accelerated population aging, there is an increasingly interest in developing solutions for the elderly living assistance. The Internet of Things is a new reality that is completely changing our everyday life, and promises to revolutionize modern healthcare by enabling a more personalized, preventive and collaborative form of care. Aiming to combine these two important topics, this work presents an IoT-ready solution for the elderly living assistance which is able to monitor and register patients vital information as well as to provide mechanisms to trigger alarms in emergency situations. Its effective low-power/low-cost and wireless characteristics turns this solution suitable to be used anywhere and by anyone, in a discrete and comfortable wristband. Experiments demonstrated a good system performance for the implemented functionalities, and regarding the autonomy we obtained an average battery lifetime of 306 hours (around 12 days). For the working range, the system have proved to perform well within a range of 60 meters before the out-of-range warning being triggered.

Wireless Sensor Networks are an emerging technology which has been recently adopted in many applications. Due to its wireless nature, the analysis of the radio propagation models plays an important role for performance evaluation in both theoretical and practical aspects. In this regards, path loss exponent is one of the most important parameter which has been considered widely in wireless communications analysis. There are several theoretical evaluations of path loss exponent for wireless sensor networks available in the literature. However there is a lack of experimental evaluation of both path loss exponent and the effect of shadowing. In this paper, three environments (free space, in building and industrial), where wireless sensor nodes are widely deployed, have been chosen in order to evaluate the experimental analysis. Path loss and path loss exponent are measured by means of Received Signal Strength Indicator (RSSI) and based on them, the standard deviation of shadowing effect is also calculated. All the measured parameters are compared with the theoretical analysis available in the literatures.

The interest in developing and deploying fully autonomous vehicles on our public roads has come to a full swing. Driverless capabilities, widely spread in modern vehicles through advanced driver-assistance systems (ADAS), require highly reliable perception features to navigate the environment, being light detection and ranging (LiDAR) sensors a key instrument in detecting the distance and speed of nearby obstacles and in providing high-resolution 3D representations of the surroundings in real-time. However, and despite being assumed as a game-changer in the autonomous driving paradigm, LiDAR sensors can be very sensitive to adverse weather conditions, which can severely affect the vehicle’s perception system behavior. Aiming at improving the LiDAR operation in challenging weather conditions, which contributes to achieving higher driving automation levels defined by the Society of Automotive Engineers (SAE), this article proposes a weather denoising method called Dynamic light-Intensity Outlier Removal (DIOR). DIOR combines two approaches of the state-of-the-art, the dynamic radius outlier removal (DROR) and the low-intensity outlier removal (LIOR) algorithms, supported by an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, DIOR can outperform state-of-the-art outlier removal solutions, achieving better accuracy and performance while guaranteeing the real-time requirements.

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.