Queues in ski resort graphs: the Ski-Optim Model

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.It is rather unknown how skiers move inside ski areas. However, new data collection systems, such as RFID chips on ski passes (which allow counting skiers at the gates of the cableways), can be used to analyse the movement of skiers in the cableways network and in the ski runs graph. This will show how queues arise at the cableways departures and how crowds are formed on the ski runs. This short paper is reporting a multi-agent simulation approach called Ski-Optim to study graphs and queues arising in a ski area. A software simulation was experimented on the ski area of Verbier in Switzerland

A new approach for robust road marking detection and tracking applied to multi-lane estimation

International audienceIn this paper, an original and inovative algorithm for multi-lane detection and estimation is proposed. Based on a three-step process, (1) road primitives extraction, (2) road markings detection and tracking, (3) lanes detection and estimation. This algorithm combines several advantages at each processing level and is quite robust to the extraction method and more specifically to the choice of the extraction threshold. The detection step is so efficient, by using robust poly-fitting based on the point intensity of extracted points, that correction step is almost not necessary anymore. This approach has been used in several project in real condition and its performances have been evaluated with the sensor data generated from SiVIC platform. This validation stage has been done with more than 2500 simulated and realistic images. . Results are very encouraging : more than 95% of marking lines are detected for less than 2% of false alarm, with 3 cm accuracy at a range of 60 m

Adaptative perception architecture for multi-lane detection and tracking

Cette thèse s'inscrit dans le cadre de la détection de marquages pour un véhicule autonome. Notre objectif est la réalisation d'un algorithme capable de détecter les différents marquages routiers liés aux voies de circulation à l'aide d'une caméra. Pour ce faire, nous avons proposé une approche fondée sur un système multi-agents avec des agents réactifs. Cette approche permet de faire évoluer les agents dans la direction des zones ayant des similitudes avec un marquage routier ainsi qu'une forte intensité lumineuse. Cette approche procède par propagation du bas vers le haut de l'image à l'inverse de la majorité des méthodes rencontrées dans la littérature. La trace de chaque agent est ensuite lissée grâce à des splines de lissages cubiques pondérées par les niveaux de confiance associés aux pixels parcourus par ces derniers. On obtient ainsi les estimations des marquages routiers. Une seconde partie du travail consiste à identifier les tirets de marquages routiers et à les suivre temporellement afin de rendre plus robuste le processus de détection ainsi que de catégoriser les marquages routiers. Cette méthode nous permet ainsi de supprimer toutes les parties incohérentes des traces des agents ainsi que de détecter et de réagir en conséquence aux décrochages de l'agent au marquage. Enfin, des expérimentations sur des données réelles et artificielles ont été réalisées afin de comparer l'approche proposée avec des travaux de recherche et des prototypes industriels. Ces expérimentations prouvent l'efficacité des méthodes par propagation en général, et de l'approche proposée en particulier.This thesis is part of the road marking detection for autonomous vehicle.Our objective is the realisation of an algorithm that detects road markings linked to lanes with a video camera. In order to achieve this, we proposed an approach based on a multi-agents system, with active agents. In this system, agents evolve in the direction of bright areas with similarities to a road marking. This approach proceeds by the propagation from bottom to top of the image in contrast to the majority of methods found in literature.The track of each agent is then (smoothed) (fitted) by smoothing cubic splines weighted by the confidence levels associated to the pixels traveled (by them). That way, the estimation of road markings is obtained for each lane. A second part of the work is to identify lane markers of each line and to track them over time in order to improve the robustness of the detection process and to categorize the road markings. This method allows us to remove all incoherent parts of the agents tracks and to detect and to react accordingly when the agent goes out of the road line marking. Finally, experiments on real and artificial data were conducted to compare the proposed approach with research algorithm and industrial prototypes. These experiments demonstrate the effectiveness of propagation methods in general and also our specific approach

Simulated annealing-optimized trajectory planning within non-collision nominal intervals for highway autonomous driving

This article considers the problem of near-optimal trajectory generation for autonomous vehicles on highways. The goal is to select a predictive reference trajectory in the free evolution space, while avoiding both generating a pre-calculated set of candidate trajectories and decoupling path and velocity optimizations. Moreover, this trajectory aims at optimizing a decision process based on multi-criteria functions, which are not straightforward to design and can have a blackbox formulation. The main idea of this article is to use the decision evaluation function in the trajectory generator with a Simulated Annealing (SA) approach. The parameters of a sigmoid trajectory are optimized within Non-Collision Nominal Intervals (NCNI), which are defined as collision-free intervals under nominal conditions using a velocity-space representation

Generator of Road Marking Textures and associated Ground Truth Applied to the evaluation of road marking detection

Abstract—To increase driving safety, many researcher works on Advanced Driving Assistance Systems (ADAS) have been developed and embedded in real prototypes during the last decades. For some of these applications like Lane Keeping System, lane perception is an essential task. For others applications like Emergency Brake Assist, lane perception modules provide useful information helping the system to select only the most dangerous obstacles. Proposed solutions to perform lane detection become more and more elaborated, however no generic solution has been proposed to calculate performances of these algorithms. Lots of solutions have been proposed to perform this lane detection. However, no generic solution has actually been proposed to quantify the quality of such applications. It is appearing that this evaluation task is now very important and critical. Most of the existing evaluation stages can be classified in two main parts. In the first case, evaluation is based on natural images databases with ground truth of road marking and/or geometrical truth of lanes. In the second case, evaluation uses virtual data and simulated images. The first one is relatively hard to perform because it is based on manual labeling of natural images. The second one has automatic labeling clustering but a realistic virtual environment is required and more precisely both realistic road bitumen and road marking textures. This paper presents an efficient solution in order to simulate roads environment for the evaluation stage of road marking detection algorithms. Moreover, a powerful tool dedicated to the road marking texture generation is proposed. It takes into account both imperfection and wear of the road marking. A virtual database using this tool will be applied on a set of road marking extractor to validate the evaluation process with our virtual approach. I I

A review of motion planning for highway autonomous driving

Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people's way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues

An Interactive Game Theory-PSO Based Comprehensive Framework for Autonomous Vehicle Decision Making and Trajectory Planning

International audienceThe mutual dependence between autonomous vehicles and human drivers is an open problem for the safety and feasibility of autonomous driving. This paper introduces a game-theoretic trajectory planner and decision-maker for mixed-traffic environments. Our solution accounts for interaction withthe surrounding vehicles while making decisions, and uses a clothoid interpolation method to generate human-like trajectories. The Particle Swarm Optimizer (PSO) used here bridges the decision-making and the trajectory generating processes for a joined execution. We chose an unsignalized intersection crossing scenarios to demonstrate the feasibility of our method. Testing results show that our approach reduces the dimension of the search space for the trajectory optimization problem and enforces geometric constraints on path curvatur

A study on AI-based approaches for high-level decision making in highway autonomous driving

Autonomous driving relies on a wide range of domains of research. It faces rapid technological and theoretical advances, with various methods and process developments. For the interest of the high-level decision making subpart of autonomous vehicle architecture, the previous states of the art report a vast literature, from traditional mobile robotics to human-modelling approaches. The purpose of this paper is to survey the current and major algorithms in the specific field of artificial intelligence for autonomous vehicles. Such systems are particularly suited for high-level decision making since they must, by definition, be able to perceive and react to their environment in order to reach given objectives. The scope is reduced to highway driving applications, considering individual, collective, and cooperative decisions. Strengths and limitations of the reviewed methods are compared, with respect to the structure and constraints of the studied driving situations. Open questions are proposed as a reflection towards the next generation of decision-makers for autonomous vehicles

A Cooperative Fusion Architecture for Robust Localization: Application to Autonomous Driving

International audienceThe localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach using a single camera. Both algorithms are designed individually as cooperative fusion processes where other sensors (GPS and proprioceptive information) and dedicated maps are integrated to strengthen the advantages of each system. The whole architecture is formalized around key components (ego-vehicle, roadway, obstacle and environment). A final decision layer, that takes into account the state of each algorithm, allows the system to choose the most appropriate ego-vehicle localization mean based on the current road situation and the environmental context