Real-time and accurate perception of dynamic objects is crucial for autonomous driving. To better capture the motion information of objects, some methods now employ 4D Doppler point clouds collected by frequency-modulated continuous-wave (FMCW) LiDAR to enhance the detection and tracking of moving objects. Compared to standard time-of-flight (ToF) LiDAR, FMCW LiDAR can provide the relative radial velocity of each point through the Doppler effect, offering a more detailed understanding of an object’s motion state. However, despite the proven efficacy of these methods, ablation studies reveal that the direct contribution of Doppler velocity to tracking is limited, with performance gains often resulting from improved object recognition and labeling accuracy.Revisiting the role of Doppler velocity, this study proposes DopplerTrack, a simple yet effective learning-free tracking method tailored for FMCW LiDAR. DopplerTrack harnesses Doppler velocity for efficient point cloud preprocessing and object detection with O(N) complexity. Furthermore, by exploring the potential motion directions of objects, it reconstructs the full velocity vector, enabling more direct and precise motion prediction. Extensive experiments on four datasets demonstrate that DopplerTrack outperforms existing learning-free and learning-based methods, achieving state-of-the-art tracking performance with strong generalization across diverse scenarios. Moreover, DopplerTrack runs efficiently at 120 Hz on a mobile CPU, making it highly practical for real-world deployment. The code and datasets have been released at https://github.com/12w2/DopplerTrack.

With the advancement of deep learning, the learning-based motion planning (MP) approach exhibits immense potential in intelligent vehicles (IVs). Because the principle and framework of the learning-based MP method differ from the traditional MP methods, exploring effective strategies to enhance interpretability plays an important role. This survey fills the gaps in the IV field’s learning-based motion planning and interpretability enhancement. Our study aims to explore two fundamental inquiries. Firstly, how can we design learning-based MP to achieve high performance? Secondly, how can we enhance the interpretability of learning-based MP? To this end, this paper provides an extensive overview of more than 200 papers employed in learning-based MP techniques within the last 10 years. By summarizing these techniques, a taxonomy for integrating learning-based MP techniques into an IV architecture is presented as three modes: learning-based key-module generator, learning-based trajectory generator, and learning-based policy generator. Interpretability enhancement has different considerations for different modes. Additionally, we compile a summary of resources utilized in learning-based MP. Finally, we discuss critical challenges and make suggestions.

Considering the long charging time and limitation of available charging stations in the traditional battery recharging facilities, the construction of battery swapping station (BSS) has become a new paradigm to satisfy the energy demand timely and sufficiently. Previous solutions lack a joint consideration of battery swapping station establishment and request assignment with the long distance subsidy. In this paper, we study the Station Placement and Assignment (SPA) problem to minimize the overall operation cost. Unfortunately, it shows great difficulty due to the infinite candidate locations and the complex coupling relation for request assignments. To address these challenges, we first devise the bundle generation strategy to reduce the infinite candidate location to finite, then propose an efficient algorithm based on the greedy strategy to assign the battery swapping requests. Extensive evaluations are carried out to show the outstanding performance of our proposed algorithms.

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge Computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by ‘frame’ is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by ‘time slot’ is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity.

The breakthrough of Wireless Power Transfer (WPT) technique provides a promising paradigm to tackle the energy limitation problem for end-devices when replenishing energy wirelessly without the need of replacing battery. Existing works seldom consider the mobility of rechargeable devices like miniature sensors on-body or implanted medical devices which may induce great gap between practical energy supply and demand. In this paper, we study the novel issue of Charging on the Move (CM) to optimize the scheduling of transmitting power of static chargers for mobile devices. Unfortunately, solving this problem is non-trivial, because it involves nonlinearity due to time-varying distances caused by movement. Besides, charging scheduling with tunable power level is a variant of budgeted maximum coverage problem, which is NP-hard. To address CM, we approximate the variational charging power as piecewise constant power, and divide the movement trajectories with approximated charging utility. Then, we first consider our problem with fixed power level, where each charger can be scheduled off or on at a fixed power level. We prove the submodularity of the objective function and design a .. approximation algorithm. On this basis, we further bound the performance loss during the problem reformulation, and finally propose a $\frac{{1 - 1/e}}{{2\left( {1 + \varepsilon } \right)T}}$ approximation algorithm for tunable scheduling strategy, where T is the maximum power level. Extensive simulations and trace-driven evaluations are conducted to evaluate the performance of our proposed algorithm.

In this paper, we propose to fuse the geometric information of a 3-D LiDAR and a monocular camera to detect the urban road region ahead of an autonomous vehicle. Our method takes advantage of both the high definition of 3-D LiDAR data and the continuity of road in image representation. First, we obtain an efficient representation of LiDAR data and an organized 2-D inverse depth map, by projecting the 3-D LiDAR points onto the camera's image plane. Through the new representation, we can acquire the intermediate representations of road scenes by extracting the vertical and horizontal histograms of the normalized inverse depth. The approximate road regions can be quickly estimated with both histogram-based schemes. To accurately find the road area, we propose a row and column scanning strategy in the approximate road region to refine the detected road area. We have carried out experiments on the public KITTI-Road benchmark, and have achieved one of the best performances among the LiDAR-based road detection methods without learning procedure.

The 2017 Intelligent Vehicle Future Challenge of China (IVFC) was held in Changshu between 24th November and 26th November, 2017. As the ninth series of this event, last year's competition has introduced many new features and has attracted 21 teams to join this competition. The HQ3 autonomous vehicle, jointly developed by National University of Defense Technology, Jilin University and Central South University, took part in this competition. This paper mainly describes the key modules of HQ3, including GPS-free localization, environment perception and behavior planning. All of these modules together enable HQ3 to perform well during the competition.

In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large scale and challenging Chinese Traffic Sign Detection benchmark is constructed. DCCF and other related methods are evaluated on this dataset. The dataset and baselines are available online.

In this paper, we propose to fuse the LIDAR and monocular image in the framework of conditional random field to detect the road robustly in challenging scenarios. LIDAR points are aligned with pixels in image by cross calibration. Then boosted decision tree based classifiers are trained for image and point cloud respectively. The scores of the two kinds of classifiers are treated as the unary potentials of the corresponding pixel nodes of the random field. The fused conditional random field can be solved efficiently with graph cut. Extensive experiments tested on KITTI-Road benchmark show that our method reaches the state-of-the-art.

Many road detection algorithms require pre-learned information, which may be unreliable as the road scene is usually unexpectable. Single image based (i.e., without any pre-learned information) road detection techniques can be adopted to overcome this problem, while their robustness needs improving. To achieve robust road detection from a single image, this paper proposes a general road shape prior to enforce the detected region to be road-shaped by encoding the prior into a graph-cut segmentation framework, where the training data is automatically generated from a predicted road region of the current image. By iteratively performing the graph-cut segmentation, an accurate road region will be obtained. Quantitative and qualitative experiments on the challenging SUN Database validate the robustness and efficiency of our method. We believe that the road shape prior can also be used to yield improvements for many other road detection algorithms.

In recent years, several kinds of tree structures for dynamic programming have been proposed. All the former trees only include pixels of the image. While in this paper, a new type of tree, which includes all the edges in the image, is constructed. In addition, weighted dynamic programming is proposed in order to improve the conventional dynamic programming. The weighted dynamic programming here is used to optimize the energy function of the new tree structure. Experiments show that our algorithm produces quite smooth and reasonable disparity maps which are close to the state-of-art. Evaluation on the Middlebury dataset shows that our method rank top in all the dynamic programming based stereo matching algorithms, even better than the algorithms that apply segmentation.

This paper describes a novel approach for fast detecting small maritime objects in infrared (IR) images. It is based on the local minimum patterns (LMP), which are theoretically the approximations of some stationary wavelet transforms (SWT). Using LMP to estimate the background with a single image, we obtain an object-aware saliency map by background subtraction. Regions of potential objects are then segmented by an adaptive threshold based on the histogram of the saliency map. We finally propose a fast clustering algorithm for localizing objects from segmented regions. Extensive experiments on challenging data sets show a competitive performance.

This paper proposes a fast and robust algorithm for traffic sign detection and recognition. The algorithm includes two stages: traffic sign detection and recognition. In the first stage, Adaboost algorithm based red pixels model of speed limit sign in the Lab color space is built. Then the model is used to extract area of latent speed limit signs. After that, the improved Hough Transform is used to locate the signs precisely. In the second stage, the template matching algorithm is used to recognize the traffic sign. At the same time, a new method of rejecting non-signs is presented, which improved the recognition rate in the complex outdoor scenes.