Abstract

International audience; This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows:- Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology.- Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure.- Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles.- Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance.- Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment.- Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed.- Sections 62.7 and 62.8 conclude the chapter with a discussion of future prospects, and provide references to further reading and additional resources.

Abstract

International audience; In the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle.

Abstract

International audience; This paper describes the development of a five degree of freedom towed underwater vehicle designed for condition monitoring of undersea pipelines. Its innovative design, with 6 fins actuated by 5 motors adding heave and sway capabilities to the yaw, roll and pitch movements possible in traditional designs, is explained. The parameters influencing the stability and the design in general are analyzed. A Control strategy for this design of highly coupled movements and control modes encompassing all the operational scenarios, are presented.

Abstract

International audience; Modern vehicles embed an increasing number of Advanced Driving Assistance Systems (ADAS). Whilst such systems showed their capability to improve comfort and safety, most of them provide assistance only as a last resort, that is, they alert the driver or trigger automatic braking only when collision is imminent. This limitation is mainly due to the difficulty to accurately anticipate risk situations in order to provide the driver with preventive assistance, i.e. assistance allowing for comfortable reaction. This paper presents a Bayesian framework which aims to detect risk situations sufficiently early to trigger conventional curative assistance as well as preventive assistance. By taking into consideration the context, the vehicle state, the driver actuation and the manner how the driver usually negotiates given situations, the framework allows to infer which type of assistance is the most pertinent to be provided to the driver. The principles of this framework are applied to a fundamental case study, the arrival to a stop intersection. Results obtained from data recorded under controlled conditions are presented. They show that the framework allows to coherently detect risk situations and to identify what assistance, including preventive assistance, is the most appropriate for the situation.

Abstract

International audience; Vision systems provide a large functional spectrum for perception applications and, in recent years, they have demonstrated to be essential in the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles. In this context, this paper presents an on-road objects detection ap-proach improved by our previous work in defining the traffic area and new strategy in obstacle extraction from U-disparity.Then, a modified particle filtering is proposed for multiple object tracking . The perception strategy of the proposed vision-only detection system is structured as follows : First, a method based on illuminant invariant image is employed at an early stage for free road space detection. A convex hull is then constructed to generate a region of interest (ROI) which includes the main traffic road area. Based on this ROI, an U-disparity map is built to characterize on-road obstacles. In this approach, connected regions extraction is applied for obstacles detection instead of standard Hough Transform. Finally, a modified particle filter framework is employed for multiple targets tracking based on the former detection results. Besides, multiple cues, such as obstacle's size verification and combination of redundant detections, are em-bedded in the system to improve its accuracy. Our experimental findings demonstrates that the system is effective and reliable when applied on different traffic video sequences from a public database.

Abstract

International audience; The aim of this paper is to detect and follow the pipeline in sonar imagery. This work is performed in two steps. The first is to split an image (first experiment) or an transformed line image of pipeline image (second experiment) into regions of uniform texture using the Gray Level Co-occurrence Matrix Method (GLCM). The second addresses the unsupervised learning method based on the Artificial Neural Networks (Self-Organizing Map or SOM) used for determining the comparative model of pipeline from the image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization for detecting the position of pipeline. After a brief review of both techniques (GLCM and SOM), we will present our methods and some results from several experiments on the real world data set.

Abstract

International audience; Integrity of the information provided by a perception system is crucial for advanced driver assistance systems intended for safety applications, like obstacle avoidance systems. A method to ensure integrity is to use different kinds of perception sources. Lidars are key sensors for multiple objects detection and tracking. Stereo vision systems (SVS) can be used to improve the tracking but, in this paper, we use also SVS to confirm the real existence of potential obstacles thanks to 3D dense reconstruction in focused regions of interest. Synchronization issues between the different sensors are addressed using predictive filtering. The proposed approach is evaluated in real conditions thanks to five use cases relevant to urban situations. Results show that this visual confirmation strategy is efficient.

Abstract

International audience; Reliable obstacle detection and localization is a key issue for driver assistance systems, particularly in urban environments. In this study a multi-modal perception approach is investigated, the objective being to enhance vehicle localization and dynamic object tracking in a world-centric map. A 3D ego-localization is achieved by merging information from a stereo vision system and data obtained from vehicle sensors. Mobile objects are detected using a multi-layer lidar that is also used to identify a constrained search space within the multiple target tracking process. Object localization and tracking is then performed in the fixed frame, which facilitates analysis and understanding of the scene. Experimental results using real world data are performed to evaluate the performance of the multi-modal system, and these are presented to show the effectiveness of the approach.

Abstract

The paper considers the problem of using an autonomous underwater platform equipped with a scanning profiler sonar to track elongated objects lying on the sea floor, e.g., pipelines. The algorithm presented assumes the existence of features that enable the discrimination between sonar profiles received from the tracked object and those received from the background sea bottom. The algorithm does not require explicit classification of the profiles received (as coming from the tracked object or from the sea bottom), and does not rely on reconstruction of the geometry of the surface of the tracked object (allowing tracking of proud or partially buried objects). Simulation results illustrate the performance of the algorithm presented, as well as the effect of departures from the assumed geometric and reflectivity characteristics of the actual tracking configuration. Results of processing of real sonar profiles obtained at sea with a man-made object of cylindrical geometry are presented, demonstrating the appropriateness of the approach proposed

Abstract

International audience; Understanding driver behaviors is an important need for the Advanced Driver Assistance Systems. In particular, the pedestrian detection systems become extremely distracting and annoying when they inform the driver with unnecessary warning messages. In this paper, we propose to study the driver behaviors whenever a pedestrian appears in front of the vehicle. A method based on the driving actions and the Hidden Markov Model (HMM) algorithm is developed to classify the driver awareness of pedestrian and the driver unawareness of pedestrian. The method is successfully validated using the collected data from the experiments that are conducted on a driving simulator. Furthermore, two simple methods based on the static parameters such as the Time-To-Collision and the Required Deceleration Parameter are also applied to our problem and are compared to the proposed method. The result shows a significant improvement of the HMM-based method compared to the simple ones.