Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.
Bird-view representation for autonomous driving decision making is proposed recently to reduce the complexity of visual features @cite_18 @cite_5 @cite_1 . They generally compress information of the map and objects to a rasterized image. Direct RGB images are very high dimensional, one can use variational auto-encoder (VAE) @cite_25 @cite_11 for dimension reduction, which learns a low dimensional latent representation of the image via an unsupervised style.
@cite_18 @cite_1 @cite_5 @cite_25 @cite_11
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For autonomous vehicles, the ability to detect and localize surrounding vehicles is critical. It is fundamental for further processing steps like collision avoidance or path planning. This paper introduces a convolutional neural network- based vehicle detection and localization method using point cloud data acquired by a LIDAR sensor. Acquired point clouds are transformed into bird's eye view elevation images, where each pixel represents a grid cell of the horizontal x-y plane. We intentionally encode each pixel using three channels, namely the maximal, median and minimal height value of all points within the respective grid. A major advantage of this three channel representation is that it allows us to utilize common RGB image-based detection networks without modification. The bird's eye view elevation images are processed by a two stage detector. Due to the nature of the bird's eye view, each pixel of the image represent ground coordinates, meaning that the bounding box of detected vehicles correspond directly to the horizontal position of the vehicles. Therefore, in contrast to RGB-based detectors, we not just detect the vehicles, but simultaneously localize them in ground coordinates. To evaluate the accuracy of our method and the usefulness for further high-level applications like path planning, we evaluate the detection results based on the localization error in ground coordinates. Our proposed method achieves an average precision of 87.9 for an intersection over union (IoU) value of 0.5. In addition, 75 of the detected cars are localized with an absolute positioning error of below 0.2m. This paper proposes the Top-View Transformation Model for image coordinate transformation, which involves transforming a perspective projection image into its corresponding bird's eye vision. A fitting parameters searching algorithm estimates the parameters that are used to transform the coordinates from the source image. Using this approach, it is not necessary to provide any interior and exterior orientation parameters of the camera. The designed car parking assistant system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car. The processing time can be reduced by storing and using the transformation matrix estimated from the first image frame for a sequence of video images. The transformation matrix can be stored as the Matrix Mapping Table, and loaded into the embedded platform to perform the transformation. Experimental results show that the proposed approaches can provide a clearer and more accurate bird's eye view to the vehicle driver. The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result. This paper proposes an adaptive Inverse Perspective Mapping (IPM) algorithm to obtain accurate bird's-eye view images from the sequential images of forward looking cameras. These images are often distorted by the motion of the vehicle; even a small motion can cause a substantial effect on bird'seye view images. In this paper, we propose an adaptive model for the IPM to accurately transform camera images to bird'seye view images by using motion information. Using motion derived from the monocular visual simultaneous localization and mapping (SLAM), experimental result shows that the proposed approaches can provide stable bird's-eye view images, even with large motion during the drive.