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In the high-mobility scenarios of next-generation wireless communication systems (beyond 5G/6G), the performance of orthogonal frequency division multiplexing (OFDM) deteriorates drastically due to the loss of orthogonality between the subcarriers caused by large Doppler frequency shifts. Various emerging waveforms have been proposed for fast time-varying channels with excellent results. In this paper, we classify these waveforms from the perspective of their modulation domain and establish a unified framework to provide a comprehensive system structure comparison. Then we analyze bit error rate (BER) performance of each waveform in doubly selective channels. Through the discussions on their complexity and compatibility with OFDM systems, we finally give the candidate waveform suggestions.

To realize the autonomous operation of a terraced rice sowing robot, a set of sowing robot operation path planning algorithms with universal significance for small, irregular terraced plots is proposed. According to the characteristics of terraces and the agronomic requirements of sowing seeding, the operation path mainly includes parallel operation and boundaries surrounding the operation path. The boundary pre-collision detection method is expounded, and the cyclic detection method judges the U-turn area. The Bézier curve fitting algorithm was used to smooth the boundary wrapping path. To verify the feasibility of the algorithm, four typical irregular small fields located in 666.7-hectare terraces of Sama in Hong He Prefecture, Yunnan Province, were randomly selected, and a field map was obtained through Google Earth. An existing seeding robot was used as a model, and the simulation and comparison tests were carried out with the mainstream EHNS algorithm and boundary polyline algorithm under the ROS-kinetic platform in the Cen Village Scientific Research Base of South China Agricultural University. The actual boundaries of the four fields with the same simulation test were used as the map to verify the field experiment. The simulation test results show that the area coverage of the sowing operation is greater than 93.53% and the replay rate is less than 3.46%, and the field test results show that the area coverage of the sowing operation is greater than 94.33% and the replay rate is less than 3.03%. The simulation test is in good agreement with the field test results, indicating that the algorithm has good adaptability, which meets the requirements of a sowing robot for sowing operation path planning and can provide a certain reference for the path planning of irregular field operation robots in hilly and mountainous areas.

In this paper, we consider the one-sided and the two-sided first exit problem for a jump diffusion process with semimartingale local time. Denote this process by $$X=\{X_{t},t\ge 0\}$$ and set $$\tau _{l}=\inf \{t\ge 0, X_{t}\le l\}$$ and $$\tau _{l,u}=\inf \{t\ge 0, X_{t}\notin (l,u)\}$$ with $$l

We explore the first passage problem for sticky reflecting diffusion processes with double exponential jumps. The joint Laplace transform of the first passage time to an upper level and the corresponding overshoot is studied. In particular, explicit solutions are presented when the diffusion part is driven by a drifted Brownian motion and by an Ornstein–Uhlenbeck process.

We present a learning-based planner that aims to robustly drive a vehicle by mimicking human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to manipulate the input to our imitation learning network freely. With that in mind, we propose a novel feedback synthesizer for data augmentation. It allows our agent to gain more driving experience in various previously unseen environments that are likely to encounter, thus improving overall performance. This is in contrast to prior works that rely purely on random synthesizers. Furthermore, rather than completely commit to imitating, we introduce task losses that penalize undesirable behaviors, such as collision, off-road, and so on. Unlike prior works, this is done by introducing a differentiable vehicle rasterizer that directly converts the waypoints output by the network into images. This effectively avoids the usage of heavyweight ConvLSTM networks, therefore, yields a faster model inference time. About the network architecture, we exploit an attention mechanism that allows the network to reason critical objects in the scene and produce better interpretable attention heatmaps. To further enhance the safety and robustness of the network, we add an optional optimization-based post-processing planner improving the driving comfort. We comprehensively validate our method's effectiveness in different scenarios that are specifically created for evaluating self-driving vehicles. Results demonstrate that our learning-based planner achieves high intelligence and can handle complex situations. Detailed ablation and visualization analysis are included to further demonstrate each of our proposed modules' effectiveness in our method., 8 pages, 10 figures, Accepted by IROS 2021

In this paper we derive several explicit results on one special sticky diffusion process which is constructed as a time-changed version of a diffusion with no sticky points. A theorem concerning the process-related Green operators defined on some nonnegative piecewise continuous functions is provided. Then, based on this theorem, we explore the distributional properties of the sticky diffusion. A financial application is presented where we compute the value of the European vanilla call option written on the underlying with sticky price dynamics.

Environmental fluctuations pose crucial challenges to a localization system in autonomous driving. We present a robust LiDAR localization system that maintains its kinematic estimation in changing urban scenarios by using a dead reckoning solution implemented through a LiDAR inertial odometry. Our localization framework jointly uses information from complementary modalities such as global matching and LiDAR inertial odometry to achieve accurate and smooth localization estimation. To improve the performance of the LiDAR odometry, we incorporate inertial and LiDAR intensity cues into an occupancy grid based LiDAR odometry to enhance frame-to-frame motion and matching estimation. Multi-resolution occupancy grid is implemented yielding a coarse-to-fine approach to balance the odometry’s precision and computational requirement. To fuse both the odometry and global matching results, we formulate a MAP estimation problem in a pose graph fusion framework that can be efficiently solved. An effective environmental change detection method is proposed that allows us to know exactly when and what portion of the map requires an update. We comprehensively validate the effectiveness of the proposed approaches using both the Apollo-SouthBay dataset and our internal dataset. The results confirm that our efforts lead to a more robust and accurate localization system, especially in dynamically changing urban scenarios.

We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving., Comment: 19 pages, 4 figures, Accepted by ECCV 2020

We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either prone to unstable matching caused by severe appearance or lighting changes, or too scarce to deliver constant and robust localization results in challenging scenarios. In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural network. Furthermore, our learned feature descriptors are demonstrated to be competent to establish robust matches and therefore successfully estimate the optimal camera poses with high precision. We comprehensively validate the effectiveness of our method using a freshly collected dataset with high-quality ground truth trajectories and hardware synchronization between sensors. Results demonstrate that our method achieves a competitive localization accuracy when compared to the LiDAR-based localization solutions under various challenging circumstances, leading to a potential low-cost localization solution for autonomous driving.

This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulate the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67% failure rate decrease with respect to initial conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is available at https://github.com/ApolloAuto/apollo.

We present L3-Net - a novel learning-based LiDAR localization system that achieves centimeter-level localization accuracy, comparable to prior state-of-the-art systems with hand-crafted pipelines. Rather than relying on these hand-crafted modules, we innovatively implement the use of various deep neural network structures to establish a learning-based approach. L3-Net learns local descriptors specifically optimized for matching in different real-world driving scenarios. 3D convolutions over a cost volume built in the solution space significantly boosts the localization accuracy. RNNs are demonstrated to be effective in modeling the vehicle's dynamics, yielding better temporal smoothness and accuracy. We comprehensively validate the effectiveness of our approach using freshly collected datasets. Multiple trials of repetitive data collection over the same road and areas make our dataset ideal for testing localization systems. The SunnyvaleBigLoop sequences, with a year's time interval between the collected mapping and testing data, made it quite challenging, but the low localization error of our method in these datasets demonstrates its maturity for real industrial implementation.

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task., Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 2019

In this paper, we study the joint Laplace transform of the sticky Brownian motion on an interval, its occupation time at zero and its integrated process. The perturbation approach of Li and Zhou [The joint Laplace transforms for diffusion occupation times, Adv. Appl. Probab. 45 (2013) 1049–1067] is adopted to convert the problem into the computation of three Laplace transforms, which is essentially equivalent to solving the associated differential equations with boundary conditions. We obtain the explicit expression for the joint Laplace transform in terms of the modified Bessel function and Airy functions.

We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system., Comment: 8 pages, 6 figures, 2 tables, Accepted by ICRA 2018

The skew diffusion process is a special diffusion process. The stochastic differential equation expression of such a special process contains the symmetric local time.Due to its special property, this model is widely used in physics and biology. Starting from skew Brownian motion,this work summaries its two constructions: discrete approximation and jointly probability distribution. Based on the skew Brownian motion, we provide the definitions for the skew Ornstein-Uhlenbeck (OU) process and skew branching process, and derive their properties: transition density, first hitting time, etc.We also introduce some applications under the skew diffusion processes.

We present a real-time monocular visual odometry system that achieves high accuracy in real-world autonomous driving applications. First, we demonstrate robust monocular SFM that exploits multithreading to handle driving scenes with large motions and rapidly changing imagery. To correct for scale drift, we use known height of the camera from the ground plane. Our second contribution is a novel data-driven mechanism for cue combination that allows highly accurate ground plane estimation by adapting observation covariances of multiple cues, such as sparse feature matching and dense inter-frame stereo, based on their relative confidences inferred from visual data on a per-frame basis. Finally, we demonstrate extensive benchmark performance and comparisons on the challenging KITTI dataset, achieving accuracy comparable to stereo and exceeding prior monocular systems. Our SFM system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Our framework also significantly boosts the accuracy of applications like object localization that rely on the ground plane.

We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.

Scale drift is a crucial challenge for monocular autonomous driving to emulate the performance of stereo. This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination framework for ground plane estimation, yielding accuracy comparable to stereo over long driving sequences. Our ground plane estimation uses multiple cues like sparse features, dense inter-frame stereo and (when applicable) object detection. A data-driven mechanism is proposed to learn models from training data that relate observation covariances for each cue to error behavior of its underlying variables. During testing, this allows per-frame adaptation of observation covariances based on relative confidences inferred from visual data. Our framework significantly boosts not only the accuracy of monocular self-localization, but also that of applications like object localization that rely on the ground plane. Experiments on the KITTI dataset demonstrate the accuracy of our ground plane estimation, monocular SFM and object localization relative to ground truth, with detailed comparisons to prior art.

We present a real-time, accurate, large-scale monocular visual odometry system for real-world autonomous outdoor driving applications. This dissertation makes four important contributions: First, we demonstrate robust monocular Structure from Motion (SFM) with a series of architectural innovations including a novel epipolar searching module in a parallel thread to replenish 3D points through a series of validation mechanism, a novel combination of local and global bundle adjustment that ensures accuracy, robustness and efficiency and so on. These architectural innovations address the challenge of robust multithreading even for scenes with large motions and rapidly changing imagery. Our design is extensible for three or more parallel CPU threads. The novel epipolar searching module, operating in parallel with other threads to generate new 3D points at every frame, together with the local bundle adjustment in the primary thread, significantly boost robustness and accuracy. Secondly, we demonstrate robust monocular SFM with accuracy unmatched by prior state-of-the-art; over several kilometers, we achieve performance similar to stereo that far exceeds other monocular architectures. The key to this performance is scale drift correction using ground plane estimation that combines cues from sparse features and dense stereo. Our third contribution is a data-driven mechanism for cue combination that learns models from training data to relate observation covariances for each cue to error behavior of underlying variables. During testing, this allows per-frame adaptation of observation covariances based on relative confidences inferred from visual data. Finally, we present a framework for highly accurate 3D localization of objects like cars and a lane detection system based on our SFM poses and ground planes. Our accurate ground plane and SFM poses easily benefit 3D localization frameworks and vision-based lane detection for those applications, as demonstrated by our experiments. Our baseline SFM system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Evaluations are presented on the challenging KITTI dataset for autonomous driving, where we achieve better rotation and translation accuracy than other state-of-the-art systems

We present a real-time, accurate, large-scale monocular visual odometry system for real-world autonomous outdoor driving applications. The key contributions of our work are a series of architectural innovations that address the challenge of robust multithreading even for scenes with large motions and rapidly changing imagery. Our design is extensible for three or more parallel CPU threads. The system uses 3D-2D correspondences for robust pose estimation across all threads, followed by local bundle adjustment in the primary thread. In contrast to prior work, epipolar search operates in parallel in other threads to generate new 3D points at every frame. This significantly boosts robustness and accuracy, since only extensively validated 3D points with long tracks are inserted at keyframes. Fast-moving vehicles also necessitate immediate global bundle adjustment, which is triggered by our novel keyframe design in parallel with pose estimation in a thread-safe architecture. To handle inevitable tracking failures, a recovery method is provided. Scale drift is corrected only occasionally, using a novel mechanism that detects (rather than assumes) local planarity of the road by combining information from triangulated 3D points and the inter-image planar homography. Our system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Evaluations are presented on the challenging KITTI dataset for autonomous driving, where we achieve better rotation and translation accuracy than other state-of-the-art systems.

The ability to determine the size and shape of a room or other interior space prior to the entry of personnel can identify risks, aid in planning, and promote efficient allocation of resources. The performance of infrared sensors, acoustic sensors, frequency modulated continuous wave radar, and stereo cameras for mapping an enclosed space is evaluated. A multisensor system and a communication network are demonstrated to map accurately the interior of a building and generate a consistent solution and robust results. The average error of the system is 10 to 30 cm. Processing time for the data fusion algorithm is typically 5 to 20 sec/room to generate the floor plan for the building. Two practical examples are presented. Current limitations of the technology are discussed, and approaches to abate them are proposed.