Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.
A commonly used dataset for this work is the NGSIM highway datasets, US-101 and I-80. This dataset was collected in 2005 using multiple overhead cameras observing sections of highway. This data was taken over three sections of 15 minutes each and contains trajectories of roughly 5000 vehicles. Visual tracking techniques were used to extract vehicles trajectories from the image data at a rate of 10Hz. Given the nature of tracking vehicles from a distant camera, the data suffers from considerable tracking noise @cite_24 , and even with aggressive filtering and correction techniques, significant problems still exist such as multiple collisions @cite_7 that do not exist in the real-world sample. We overcome these limitations with a modern data collection vehicle, as later described in Section .
@cite_24 @cite_7
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We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide. This paper introduces a dataset gathered entirely in urban scenarios with a car equipped with one stereo camera and five laser scanners, among other sensors. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at a high rate (20 fps) during a 36.8 km trajectory, which allows the benchmarking of a variety of computer vision techniques. We describe the sensors employed and highlight some applications which could be benchmarked using the present work. Both plain text and binary files are provided, as well as open-source tools for working with the binary versions. The dataset is available for download at http: www.mrpt.org MalagaUrbanDataset. We describe a vehicle tracking algorithm using input from a network of nonoverlapping cameras. Our algorithm is based on a novel statistical formulation that uses joint kinematic and image appearance information to link local tracks of the same vehicles into global tracks with longer persistence. The algorithm can handle significant spatial separation between the cameras and is robust to challenging tracking conditions such as high traffic density, or complex road infrastructure. In these cases, traditional tracking formulations based on MHT, or JPDA algorithms, may fail to produce track associations across cameras due to the weak predictive models employed. We make several new contributions in this paper. Firstly, we model kinematic constraints between any two local tracks using road networks and transit time distributions. The transit time distributions are calculated dynamically as convolutions of normalized transit time distributions that are learned and adapted separately for individual roads. Secondly, we present a complete statistical tracker formulation, which combines kinematic and appearance likelihoods within a multi-hypothesis framework. We have extensively evaluated the algorithm proposed using a network of ground-based cameras with narrow field of view. The tracking results obtained on a large ground-truthed dataset demonstrate the effectiveness of the algorithm proposed. Spaceborne remote sensing videos are becoming indispensable resources, opening up opportunities for new remote sensing applications. To exploit this new type of data, we need sophisticated algorithms for semantic scene interpretation. The main difficulties are: 1) Due to the relatively poor spatial resolution of the video acquired from space, moving objects, like cars, are very difficult to detect, not to mention track; 2) camera movement handicaps scene interpretation. To address these challenges, in this paper we propose a novel framework that fuses multispectral images and space videos for spatiotemporal analysis. Taking a multispectral image and a spaceborne video as input, an innovative deep neural network is proposed to fuse them in order to achieve a fine-resolution spatial scene labeling map. Moreover, a sophisticated approach is proposed to analyze activities and estimate traffic density from 150,000+ tracklets produced by a Kanade-Lucas-Tomasi keypoint tracker. The proposed framework is validated using data provided for the 2016 IEEE GRSS data fusion contest, including a video acquired from the International Space Station and a DEIMOS-2 multispectral image. Both visual and quantitative analysis of the experimental results demonstrates the effectiveness of our approach.