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Openstreetmap Using And Enhancing The Free Map Of The World

Localization integrity consists in providing a real-time measure of the level of trust to be placed in the localization estimates as vehicles operate. It provides a means of knowing whether position estimates are usable for navigation purposes. This paper formalizes the integrity concept and its underlying principles. Vehicles operate in different navigation environments, and so multiple sensors are used to ensure the required performance. Different sources of error exist. They must be bounded according to the acceptable level of risk for the application. This paper presents a generic approach for addressing integrity. It combines measurement rejection (for measurements considered to be faults) and position error characterization. For this purpose, a multi-sensor data fusion with a Fault Detection and Exclusion algorithm is constituted using a bank of information filters. These filters allow detected faults to be isolated without any prior assumption regarding the number of simultaneous errors. In addition, external integrity is expressed as a Protection Level of the localization solution. It uses a Student's t-distribution in order to bound the distribution of the position error applicable to small integrity risks after a learning step. The approach is tested on data acquired on public roads using an experimental vehicle equipped with off-the-shelf proprioceptive and exteroceptive sensors together with an HD map. The results obtained validate the proposed approach.

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Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook

Guaranteeing the safety of an autonomous vehicle (AV) is a challenging task, especially if the perceived environment is highly uncertain and other road users deviate from their expected trajectories. In this paper, we propose a probabilistic overall strategy for risk assessment and management of AV in highway through a Sequential Level Bayesian Decision Network (SLBDN) and an appropriate analytical formalization of criteria for anomaly detection based on a Dynamic Predicted Inter-Distance Profile (DPIDP) between vehicles. Accordingly, the proposed system is designed to take the suitable maneuver decision, have a safety retrospection and verification over the current maneuver risk and take appropriate evasive action autonomously from moving obstacles. Moreover, this probabilistic framework accounts for measurements uncertainty through an Extended Kalman Filter (EKF) and for vehicles' maximum capacities. Since the proposed strategy has a short response time, integrating safety verification in the decision-making process makes real time evasive decisions possible. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for risky scenarios.

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Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird’s-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird’s-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving. The dataset will be made available at http://lanegraph.informatik.uni-freiburg.de .

Lane Graph Estimation for Scene Understanding in Urban Driving

This paper describes the design of an overall Multi-Controller Architecture (MCA) for safe automated driving, under uncertainties in highway environment. This MCA combines the decision-making process, the path planning and the control algorithms. In order to ensure the safety and smoothness of the vehicle navigation, a decision-making strategy for handling lane change maneuvers is proposed by means of a robust Two-Sequential Level Bayesian Decision Network (TSLDN). The latter is utilized for the driving situation assessment, the decision-making and for safety verification of the current performed maneuver. Moreover, from actual navigation risk assessment standpoint, a dual-safety criterion combining an Extended Time-To-Collision (ETTC) and a novel Dynamic Predicted Inter-Distance Profile (DPIDP) is developed. The DPIDP is evaluated on-line as the actual distance between vehicles during lane changes maneuvers over an observed control horizon. Thanks to this proposed algorithm, a safety retrospection over the current maneuver risk could be carried out. In this way, the overall MCA described in this paper allows the best probabilistic decision to achieve the vehicle navigation task in hazardous situations while maximizing its safety. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for very risky/complex scenarios.

Safe Autonomous Overtaking Maneuver based on Inter-Vehicular Distance Prediction and Multi-Level Bayesian Decision-Making

Road information, like lanes number, play an important role for intelligent vehicles (IV). Traditionally such road information are obtained through a vision-based measurement or by using a digital detailed map. In this paper, we present a new method for estimating the number of lanes using a low precision GPS receiver and OpenSteetMap (OSM). The method includes the integration of the GPS traces and OSM for a map matching. To this end we developed a probabilistic multicriteria algorithm for map matching that takes into account the accuracy of the GPS data and the attribute information of the road from OSM. Afterward we estimate the number of lanes from OSM. We tested our algorithm on a set of GPS data collected in an urban area near Paris for a total distance of 50km and the overall estimation accuracy reached 83.64%

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Map Matching and Lanes Number Estimation with Openstreetmap

Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most works presented in the literature, ego-localization is split into three parts: Road level-localization consisting in the road on which the vehicle travels, Lane level localization which is the lane on which the vehicle travels, and Ego lane level localization being the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with two main novelties. The first one is the proposition of a complete solution that tackles every part of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.

/pdf/end-to-end-probabilistic-ego-vehicle-localization-framework-1gq89dmds2.pdf

End-to-End Probabilistic Ego-Vehicle Localization Framework

Localizing the vehicle in its lane is a critical task for any autonomous vehicle. By and large, this task is carried out primarily through the identification of ego-lane markings. In recent years, ego-lane marking detection systems have been the subject of various research topics, using several inputs data such as camera or lidar sensors. Lately, the current trend is to use high accurate maps (HD maps) that provide accurate information about the road environment. However, these maps suffer from their availability and their price tag. An alternative is the use of affordable low-accurate maps. Yet, there is relatively little work on it. In this paper, we propose an information-driven approach that takes into account inaccurate prior geometry of the road from OpenStreetMap (OSM) to perform ego-lane marking detection using solely a lidar. The two major novelties presented in this paper are the use of the OSM datasets as prior for the road geometry, which reduces the research area in the lidar space, and the information-driven approach, which guarantees that the outcome of the detection is coherent to the road geometry. The robustness of the proposed method is proven on real datasets and statistical metrics are used to highlight our method's efficiency.

/pdf/an-information-driven-approach-for-ego-lane-detection-using-555i9g3y0d.pdf

Dieumet Denis

Papers

Safe Autonomous Overtaking Maneuver based on Inter-Vehicular Distance Prediction and Multi-Level Bayesian Decision-Making

Map Matching and Lanes Number Estimation with Openstreetmap

Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment

End-to-End Probabilistic Ego-Vehicle Localization Framework

An Information Driven Approach For Ego-Lane Detection Using Lidar And OpenStreetMap