This paper presents a global route planning algorithm designed as part of the navigation module embedded in an Autonomous Driving System (ADS). Unlike trajectory planners, which focus on local maneuvering and vehicle control, this algorithm determines optimal routes at a higher level, prioritizing dynamic adaptation to traffic conditions and regulatory elements. The planner aims to minimize travel time rather than merely reducing the total distance traveled, making it particularly effective in urban environments where traffic signals, vehicle interactions, and road regulations significantly impact journey duration. To achieve this, the algorithm dynamically adjusts to real-time variations in traffic flow and control measures. Additionally, it integrates risk-aware routing by imposing penalties on roads with higher pedestrian interaction, enhancing safety and increasing public acceptance of ADS technology. Designed for efficiency and scalability, the algorithm is lightweight enough to run on microcontroller-based embedded systems, ensuring feasibility for real-world deployment in constrained computing environments. The algorithm was tested using a Renault mass-production car, demonstrating its applicability in real-world driving scenarios.

This paper presents an approach for a car navigation system to follow a road path consisting on a sequence of lanelets. The motion control is divided into high-level planning that produces the road path and lower-level reactive control that safely follows the path. The approach presented here is the lower-level reactive control that combines the simple pure pursuit method to obtain a reference curvature with the beam curvature method (BCM) that keeps the car in the center of the free space in the lane avoiding obstacles that can partially block the lane. The whole system has been applied to an autonomous vehicle aimed for elderly or disable people.

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of RO (Range Only) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approach with RO sensors is quite incomplete. This paper presents a comparison between a filtering algorithm, the EKF, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained, first in an outdoor environment using two types of RO sensors and then in an indoor environment with WiFi sensors. The results demonstrate the feasibility of the smoothing approach with WiFi sensors in indoors.