Daniele Vignarca

The latest advancements in vehicle automation have revealed significant potential for enhancing traffic management via Advanced Driver Assist Systems (ADAS), benefiting both safety and environmental considerations. Green Light Optimal Speed Advisory (GLOSA) systems represent a significant application in the Cooperative-Intelligent Transportation System (C-ITS) field adopting Vehicle-to-Everything (V2X) communication technology. The literature nowadays addresses quite extensively the GLOSA, and C-ITS in general, for conventional vehicles like cars. At the same time, there is emerging research starting to involve also the public transportation vehicles within this framework. The focus for buses is typically posed either on the comfort and regularity of the service for passengers or on the energy consumption which is reduced by adopting suitable speed profiles for the vehicle, thus avoiding unnecessary stops. This work presents a statistical analysis based on experimental data collected in real-world urban scenarios over one entire year. The outcome of this analysis allows the design of speed profiles typical for a public transportation vehicle, accounting for features such as the bus stop station for getting passengers off and on.

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle’s distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.

The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving.

Teleoperation provides the human operator with sophisticated perceptual and cognitive skills in an over-the-network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a fallback plan. Variable network time-delay in data transmission is the major problem in teleoperating a vehicle. On 4G network, variability of this delay is significant (70-150 ms ping). Due to this, both video streaming and driving commands encounter variable time-delay. This paper presents an approach to provide the human-operator with a forecasted video stream that replicates future perspective of vehicle’s field of view accounting for the delay present in the network. Regarding the image transformation, perspective projection technique is combined with correction given by Smith predictor in the control loop. This image transformation accounts current time-delay and tries to address both issues, time-delays as well as variability. For experiment sake, only frontward field of view is forecasted. Performance is evaluated by performing vehicle teleoperation with real vehicle on street edge-case maneuvers and later comparing the cross-track error with and without perspective projection. Results obtained show improvement in path following tasks.

Teleoperation provides human operator sophisticated perceptual and cognitive skills into an over the network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a backup plan. Variable network time delays in data transmission is the major problem in teleoperating a vehicle. On 4G network, variability of these delays is high. Due to this, both video streaming and driving commands encounter variable time delay. This paper presents an approach of providing the human operator a forecast video stream which replicates future perspective of vehicle field of view accounting the delay present in the network. Regarding the image transformation, perspective projection technique is combined with correction given by smith predictor in the control loop. This image transformation accounts current time delay and tries to address both issues, time delays as well as its variability. For experiment sake, only frontward field of view is forecast. Performance is evaluated by performing online vehicle teleoperation on street edge case maneuvers and later comparing the path deviation with and without perspective projection.