Etude et réalisation d'un système de communications par lumière visible (VLC/LiFi). Application au domaine automobile.

The scientific problematic of this PhD is centered on the usage of Visible LightCommunications (VLC) in automotive applications. By enabling wireless communication amongvehicles and also with the traffic infrastructure, the safety and efficiency of the transportation canbe substantially increased. Considering the numerous advantages of the VLC technologyencouraged the study of its appropriateness for the envisioned automotive applications, as analternative and/or a complement for the traditional radio frequency based communications.In order to conduct this research, a low-cost VLC system for automotive application wasdeveloped. The proposed system aims to ensure a highly robust communication between a LEDbasedVLC emitter and an on-vehicle VLC receiver. For the study of vehicle to vehicle (V2V)communication, the emitter was developed based on a vehicle backlight whereas for the study ofinfrastructure to vehicle (I2V) communication, the emitter was developed based on a traffic light.Considering the VLC receiver, a central problem in this area is the design of a suitable sensorable to enhance the conditioning of the signal and to avoid disturbances due to the environmentalconditions, issues that are addressed in the thesis. The performances of a cooperative drivingsystem integrating the two components were evaluated as well.The experimental validation of the VLC system was performed in various conditions andscenarios. The results confirmed the performances of the proposed system and demonstrated thatVLC can be a viable technology for the considered applications. Furthermore, the results areencouraging towards the continuations of the work in this domain.La problématique scientifique de cette thèse est centrée sur le développement decommunications par lumière visible (Visible Light Communications - VLC) dans lesapplications automobiles. En permettant la communication sans fil entre les véhicules, ou entreles véhicules et l’infrastructure routière, la sécurité et l'efficacité du transport peuvent êtreconsidérablement améliorées. Compte tenu des nombreux avantages de la technologie VLC,cette solution se présente comme une excellente alternative ou un complément pour lescommunications actuelles plutôt basées sur les technologies radio-fréquences traditionnelles.Pour réaliser ces travaux de recherche, un système VLC à faible coût pour applicationautomobile a été développé. Le système proposé vise à assurer une communication très robusteentre un émetteur VLC à base de LED et un récepteur VLC monté sur un véhicule. Pour l'étudedes communications véhicule à véhicule (V2V), l'émetteur a été développé sur la base d’un pharearrière rouge de voiture, tandis que pour l'étude des communications de l'infrastructure auvéhicule (I2V), l'émetteur a été développé sur la base d'un feu de circulation. Considérant lerécepteur VLC, le problème principal réside autour d’un capteur approprié, en mesured'améliorer le conditionnement du signal et de limiter les perturbations dues des conditionsenvironnementales. Ces différents points sont abordés dans la thèse, d’un point de vue simulationmais également réalisation du prototype.La validation expérimentale du système VLC a été réalisée dans différentes conditions etscénarii. Les résultats démontrent que la VLC peut être une technologie viable pour lesapplications envisagées

A Microscopic Simulation Study of Applications of Signal Phasing and Timing Information in a Connected Vehicle Environment

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queue

Machine Learning Tools for Optimization of Fuel Consumption at Signalized Intersections in Connected/Automated Vehicles Environment

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. Reinforcement learning (RL) is a machine learning paradigm that mimics the human learning behavior where an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the Eco-driving problem, RL does not necessitate prior knowledge of the environment being learned and processed. Therefore, the aim of this study is twofold: (1) Develop a novel brute force Eco-driving algorithm (ECO-SEMI-Q) for CAV (Connected/Autonomous Vehicles) passing through semi-actuated signalized intersections; and (2) Develop a novel Deep Reinforcement Learning (DRL) Eco-driving algorithm for CAV passing through fixed-time signalized intersections. The developed algorithms are tested at both microscopic and macroscopic levels. For the microscopic level, results indicate that the fuel consumption for vehicles controlled by the ECO-SEMI-Q and DRL models is 29.2% and 23% less than that for the case with no control, respectively. For the macroscopic level, a sensitivity analysis for the impact of MPR (Market Penetration Rate) shows that the savings in fuel consumption increase with higher MPR. Furthermore, when MPR is greater than 50%, the ECO-SEMI-Q algorithm provides appreciable savings in travel times. The sensitivity analysis indicates savings in the network fuel consumption when the MPR of the DRL algorithm is higher than 35%. At MPR less than 35%, the DRL algorithm has an adverse impact on fuel consumption due to aggressive lane change and passing maneuvers. These reductions in fuel consumption demonstrate the ability of the algorithms to provide more environmentally sustainable signalized intersections