From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning

Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. In this work, we investigate these problems in the autonomous driving field, especially for a maneuver planning module for roundabout insertions. In particular, we present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios. Finally, we analyze techniques aimed at reducing the gap between simulated and real-world data showing that this increased the generalization capabilities of the system both on unseen and real-world scenarios.Comment: Intelligent Vehicle Symposium 2020 (IV2020

Scenario-Driven Search for Pedestrians aimed at Triggering Non-Reversible Systems

Abstract-This paper presents the results of an innovative approach to pedestrian detection for automotive applications in which a non-reversible system is used; therefore the aim is to reach a very low false detection rate, ideally zero, by searching for pedestrians in specific areas only. The great advantages of such an approach are that pedestrian recognition is performed on limited image areas-therefore boosting its timing performance- and no assessment on the danger level is finally required before providing the result to either the driver or an on-board computer for automatic manoeuvres. This system has been extensively tested on two prototype vehicles equipped with one laserscanner, one camera, and brakeby-wire technology both in Italy and Korea; this paper describes the extensive tests and shows performance measurements. I

Stereo obstacle detection in challenging environments: The VIAC experience

experienc

Vision-only fully automated driving in dynamic mixed-traffic scenarios

In this work an overview of the local motion planning and dynamic perception framework within the V-Charge project is presented. This framework enables the V-Charge car to autonomously navigate in dynamic mixed-traffic scenarios. Other traffic participants are detected, classified and tracked from a combination of stereo and wide-angle monocular cameras. Predictions of their future movements are generated utilizing infrastructure information. Safe motion plans are acquired with a system-compliant sampling-based local motion planner. We show the navigation performance of this vision-only autonomous vehicle in both simulation and real-world experiments

Intelligent Vehicles

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. Document type: Part of book or chapter of boo

Environment-Detection-and-Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment

Abstract—This paper presents an environment-detection-and-mapping algorithm for autonomous driving that is provided in real time and for both rural and off-road environments. Environment-detection-and-mapping algorithms have been de-signed to consist of two parts: 1) lane, pedestrian-crossing, and speed-bump detection algorithms using cameras and 2) obstacle detection algorithm using LIDARs. The lane detection algorithm returns lane positions using one camera and the vision module “VisLab Embedded Lane Detector (VELD), ” and the pedestrian-crossing and speed-bump detection algorithms return the position of pedestrian crossings and speed bumps. The obstacle detection algorithm organizes data from LIDARs and generates a local obstacle position map. The designed algorithms have been im-plemented on a passenger car using six LIDARs, three cameras, and real-time devices, including personal computers (PCs). Vehicle tests have been conducted, and test results have shown that the vehicle can reach the desired goal with the proposed algorithm. Index Terms—Autonomous driving, lane detection, obstacle de-tection, pedestrian-crossing detection, speed-bump detection. I

Système de stéréovision pour la détection d'obstacles et de véhicule en temps réel

Dans le cadre de l'aide à la conduite automobile, nous présentons deux méthodes de détection d'obstacles et de détection de véhicule à partir de notre système embarquable de stéréovision. Ces deux tâches sont effectuées en temps réel en segmentant des cartes éparses de profondeur par sélection de segments 3D. Pour la détection d'obstacles, la sélection des segments 3D s'effectue à partir du calcul de leur angle d'inclinaison. La détection de véhicule s'effectue à partir des données fournies par ARGO, le véhicule expérimental autonome développé à l'Université de Parme

IR Pedestrian Detection for Advanced Driver Assistance Systems

This paper describes a system for pedestrian detection in infrared images implemented and tested on an experimental vehicle. A specific stabilization procedure is applied after image acquisition and before processing to cope with vehicle movements affecting the camera calibration. The localization of pedestrians is based on the search for warm symmetrical objects with specific size and aspect ratio. A set of filters is used to reduce false detections. The final validation process relies on the human shapes morphological characteristics. Document type: Part of book or chapter of boo