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Cognition In The Wild

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model

Introduction - The aims of this book the features of this book organization and background the analytical mind: strengh and weakness. Fundamentals of linear algebra - Vector and matrix calculus Eigenvalue problem linear systems and optimization matrix and tensor algebra. Probabilities and statistical estimation - probability distributions manifolds and local distributions gaussian distributions and X2 distributions statistical estimation for gaussian models general statistical estimation maximum likelihood estimation Akaike information criterion. Representation of geometric objects - image points and image lines space points and space lines space planes conics space conics and quadrics coordinate transformation and projection. Geometric correction - general theory correction of image points and image lines correction of space points and space lines correction of space planes orthogonality correction conic incidence correction. 3-D computation by stereo vision - epipolar constraint optimal correction of correspondence 3-D reconstruction of points 3-D reconstruction of lines optimal back projection onto a space plane scenes infinitely far away camera calibration errors. Parametric fitting - general theory optimal fitting for image points optimal fitting for image lines optimal fitting for space points optimal fitting for space lines optimal fitting for space planes. Optimal filter - general theory iterative estimation scheme effective gradient approximation reduction from the klaman filter estimation from linear hypotheses. Renormalization - eigenvector fit unbiased eigenvector generalized eigenvalue fit renormalization lincarization second order renormalization. Applications of geometric estimation - image line fitting conic fitting space plane fitting by range sensing space plane fitting by stereo vision. 3-D motion analysis - general theory lincarization and renormalization optimal correction and decomposition reliability of 3-D reconstruction critical surfaces 3-D reconstruction from planar surface motion camera rotation and information. 3-D interpretation of optical flow - optical flow detection theoretical basis of 3-D interpretation optimal estimation of motion parameters. (Part contents).

Statistical optimization for geometric computation : theory and practice

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Shared Agency: A Planning Theory of Acting Together

In this paper, we present a localization approach that is based on a point-cloud matching method (normal distribution transform “NDT”) and road-marker matching based on the light detection and ranging intensity. Point-cloud map-based localization methods enable autonomous vehicles to accurately estimate their own positions. However, accurate localization and “matching error” estimations cannot be performed when the appearance of the environment changes, and this is common in rural environments. To cope with these inaccuracies, in this work, we propose to estimate the error of NDT scan matching beforehand (off-line). Then, as the vehicle navigates in the environment, the appropriate uncertainty is assigned to the scan matching. 3D NDT scan matching utilizes the uncertainty information that is estimated off-line, and is combined with a road-marker matching approach using a particle-filtering algorithm. As a result, accurate localization can be performed in areas in which 3D NDT failed. In addition, the uncertainty of the localization is reduced. Experimental results show the performance of the proposed method.

Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching

In this study, we developed an autonomous driving system for mountainous public roads. Three-dimensional normal distribution transform (NDT) scan matching is employed for localization and a model predictive controller is utilized for vehicle motion control. In order to increase the robustness of the localization method, the estimated poses are computed by an extended Kalman filter using dead reckoning and NDT information. The uncertainty of the pose estimated by NDT is determined by using the Hessian matrix computed in the optimization process for scan matching. We conducted experiments in a public road environment over 20 times and all of the tests were successful. The experimental results confirmed that the autonomous driving system can operate reliably in mountainous public roads. In addition, the evaluation results obtained for the localization method showed that accurate and robust localization can be achieved in mountainous rural environments.

Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning

This paper presents a model for safe driving at blind intersections and its integration to a local planner based on a Frenet frame. The model predicts potential moving obstacles from blind intersections to proactively slow down to avoid potential collisions. The derivation of the model is described and its parameters are detailed. The local planner computes smooth trajectories with smooth velocity profiles so that the vehicle can follow the paths without jerk and sudden accelerations resulting in safe and comfortable navigation. Experimental results in simulation and in the real field with an autonomous car, show that the proposed predictive driving framework can reproduce human expert driver's trajectories and velocities when facing blind intersections.

Autonomous predictive driving for blind intersections

This paper proposes a collision prediction method based on blind area traffic prediction using high definition maps and 3D LiDAR(Light Detection and Ranging). To keep safe driving, it is necessary to predict risks and to ready to avoid it. There are so many intersections with low visibility in residential area. In such environments, crossing collision accidents are often occurred. The proposed method in this paper predicts the blind area traffic using lane network information and particle filter, and updates the predicted results using visibility information of 3D LiDAR. Finally, this paper illustrates experimental results in urban environments and the calculated safe speed using proposed method is compared with expert driver data.

Blind Area Traffic Prediction Using High Definition Maps and LiDAR for Safe Driving Assist

This paper presents a model for velocity control in blind corners and intersections based on expert driver data. Accurate expert driver data was collected with a car equipped with a 3D LiDAR and high definition maps. A model based on human expert driver data is used to control the velocity of the ego-vehicle when facing blind intersections. The model regulates ego-vehicle velocity based on the visibility of the road at the blind intersection. As the vehicle approximates the intersection and crossing roads are not visible, the vehicle slows down, then as the roads become visible the vehicle accelerates. Experimental results show the performance of the velocity model compared towards 270 trajectories taken from 7 expert drivers towards 6 different intersections without mandatory stops.

Yuki Yoshihara

Papers

Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching

Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning

Autonomous predictive driving for blind intersections

Blind Area Traffic Prediction Using High Definition Maps and LiDAR for Safe Driving Assist

Proactive driving modeling in blind intersections based on expert driver data