André Campos

This article introduces the architecture and real-world deployment of a collision avoidance system based on shared perception between vehicles and infrastructure in autonomous mobility. It employs microservices on both the infrastructure and vehicle platforms to integrate data from multiple sensors and distributed computing units. By leveraging Vehicle to Infrastructure (V2I) communication, the system autonomously interfaces with vehicle’s systems in critical emergency scenarios, such as executing an emergency stop triggered by the presence of Vulnerable Road Users (VRUs), signaled by a roadside unit. Edge computing at key points enhances the system’s capabilities, enabling swift object detection through computer vision and real-time integration with the vehicle’s interfaces via V2I message exchanges. The system is integrated in both fully autonomous and semi-autonomous vehicles, performing emergency stops when a hazard is detected. Depending on the vehicle, it either returns control to the driver or allows the autonomous control system to resume operation. The integration with Autoware, the autonomous control system for the fully autonomous vehicle used in this work, highlights the system’s ability to seamlessly integrate vehicular communications into autonomous mobility. It cooperates with Autoware using information from surrounding entities to execute emergency stop maneuvers without disrupting autonomous driving. Once the maneuver is completed and the hazard is cleared, the vehicle resumes its path without manual intervention. The experimental results with both fully autonomous and semi-autonomous vehicles, in the presence of VRU, demonstrate the system’s ability to deliver timely alerts and ensure successful vehicle responses. Additionally, a comprehensive timing analysis of the system’s performance is conducted across both vehicles and infrastructure.

The automotive industry is rapidly moving towards the deployment of fully autonomous vehicles, which rely on advanced driver-assistance systems (ADAS) and robust perception systems for safe and efficient navigation. Among these technologies, Light Detection and Ranging (LiDAR) sensors have gained prominence for their ability to provide high-resolution 3D representations of the vehicle's surroundings in real-time. This capability significantly improves obstacle detection and object recognition. However, the integration of LiDAR sensors with ADAS may still face several challenges, such as image noise and data output size, slowing their widespread adoption. This article introduces the Advanced LiDAR Framework for Automotive (ALFA), a tool designed for the development and evaluation of LiDAR data processing algorithms in different setups, with a focus on embedded platforms with acceleration capabilities. ALFA enables seamless interfacing with a wide range of devices and the creation and deployment of hardware accelerators for processing point cloud data across diverse applications, facilitating the integration with the computing system of the car. In addition to presenting the framework, this article provides a comprehensive evaluation and benchmarking of ALFA, including point cloud filtering and real-time ground segmentation.

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.