Conference
IEEE Intelligent Vehicles Symposium
About: IEEE Intelligent Vehicles Symposium is an academic conference. The conference publishes majorly in the area(s): Object detection & Advanced driver assistance systems. Over the lifetime, 3896 publications have been published by the conference receiving 92839 citations.
Topics: Object detection, Advanced driver assistance systems, Vehicle dynamics, Sensor fusion, Motion planning
Papers published on a yearly basis
Papers
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05 Jun 2011
TL;DR: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control.
Abstract: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.
1,199 citations
05 Jun 2011
TL;DR: In this article, a sparse feature matcher and visual odometry algorithm are combined with a multi-view linking scheme for generating consistent 3D point clouds for online 3D reconstruction.
Abstract: Accurate 3d perception from video sequences is a core subject in computer vision and robotics, since it forms the basis of subsequent scene analysis. In practice however, online requirements often severely limit the utilizable camera resolution and hence also reconstruction accuracy. Furthermore, real-time systems often rely on heavy parallelism which can prevent applications in mobile devices or driver assistance systems, especially in cases where FPGAs cannot be employed. This paper proposes a novel approach to build 3d maps from high-resolution stereo sequences in real-time. Inspired by recent progress in stereo matching, we propose a sparse feature matcher in conjunction with an efficient and robust visual odometry algorithm. Our reconstruction pipeline combines both techniques with efficient stereo matching and a multi-view linking scheme for generating consistent 3d point clouds. In our experiments we show that the proposed odometry method achieves state-of-the-art accuracy. Including feature matching, the visual odometry part of our algorithm runs at 25 frames per second, while - at the same time - we obtain new depth maps at 3-4 fps, sufficient for online 3d reconstructions.
930 citations
04 Jun 2008
TL;DR: In this paper, a robust and real-time approach to lane marker detection in urban streets is presented, which is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANAC algorithm for fitting Bezier Splines, which was then followed by a post-processing step.
Abstract: We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.
672 citations
26 Jun 2018
TL;DR: This paper presents an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks, and performs extremely well in the challenging KITTI dataset.
Abstract: While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-to-end and performs extremely well in the challenging KITTI dataset. Our approach is also very efficient, allowing us to perform inference at more then 23 frames per second. Training scripts and trained weights to reproduce our results can be found here: https://github.com/MarvinTeichmann/MultiNet
633 citations
27 Aug 2015
TL;DR: Experimental results show the effectiveness of the proposed approach at various speeds on windy roads, and it is shown that it is less computationally expensive than existing methods which use vehicle tire models.
Abstract: We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving In particular, we analyze the statistics of the forecast error of these two models by using experimental data In addition, we study the effect of discretization on forecast error We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model The proposed approach is less computationally expensive than existing methods which use vehicle tire models Moreover it can be implemented at low vehicle speeds where tire models become singular Experimental results show the effectiveness of the proposed approach at various speeds on windy roads
621 citations