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Stochastic Processes And Filtering Theory

The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCACS) based on the model predictive control (MPC). First, the proposed LSCACS decides which control mode to be implemented based on vehicle dynamics states, including a normal driving mode, a full auto brake mode, and a brake and stability mode. The MPC is used in the upper controller to calculate the desired deceleration and additional yaw moment. The lower controller calculates the desired tire forces of four wheels and realizes them by certain wheel cylinder hydraulic pressures. The LSCACS is validated by hardware-in-the-loop (HIL) tests, and the results show LSCACS’s effectiveness and great performance of the collision avoidance and lateral stability.

Longitudinal Collision Avoidance and Lateral Stability Adaptive Control System Based on MPC of Autonomous Vehicles

The existing rear-end collision warning systems (CWS) that involve the variable perception–reaction time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different PRTs. To deal with the related problems, several studies have been conducted based on nonparametric approaches. However, the previous nonparametric methods are of doubtful validity with different PRTs. Moreover, there is a lack of consideration for the criterion to split the real-time data into training and testing sets in terms of enhancing the algorithm performance. In this paper, we propose multilayer perceptron neural-network-based rear-end collision warning algorithm (MCWA) to develop a real-time CWS without any influence of human PRTs. Through a sensitivity analysis, the optimal criterion for splitting real-time data into training and prediction is found in terms of a tradeoff between training time and algorithm accuracy. Comparison study demonstrates that the proposed algorithm outperforms other previous algorithms for predicting the potential rear-end collision by detecting severe deceleration in advance.

Real-Time Rear-End Collision-Warning System Using a Multilayer Perceptron Neural Network

To protect vulnerable road users (VRU) like children and elderly, this paper presents a new warning system on smartphones. This system has three phases. First, we propose a new geometric approach to activate the system for necessary risky situations. Second, we extract some important features for VRUs and drivers based on their smartphones sensors to estimate the collision risk by using a fuzzy inference engine. Finally, we divide the warning alarms into low risk, medium risk, and high risk. To improve system accuracy, we consider the effects of vehicle acceleration, weather condition, time of day, pedestrian age, and driver age in our system and use 4G wireless communications between VRUs and drivers. Experimental results on 608 samples from six important types of accident situations show that our system for in danger VRUs, has 96% accuracy, 63% recall, and 90% precision. The improvements in accuracy, precision, and F-measure are 5%, 70%, and 42% compared with the previous works. Moreover, the activation phase of our system led to 400-ms reduction in run time while the accuracy improves 22%. Besides, on the random samples extracted from the accident simulator software, the accuracy, recall, and precision of the proposed system improve 98%, 75%, and 60%, which are better than the previous similar systems.

Three-Phases Smartphone-Based Warning System to Protect Vulnerable Road Users Under Fuzzy Conditions

Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR) The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesn't require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging.

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.

A Lane-Level Road Marking Map Using a Monocular Camera

High Definition map (HD Map) is an important part of autonomous driving vehicle. Most conventional method to generate HD map requires expensive system and postprocessing of observed data. In this paper, we propose automatic HD map generating algorithm using just monocular camera without further human labors. The proposed algorithm detects road lane from image and classifies the type of road lane at pixel-level with Fully Convolutional Network (FCN) which outperforms the other semantic segmentation methods. The segmentation results are used to extract lane features, and the features are used for loop-closure detection. Final map is generated with graph-based Simultaneous Localization and Mapping (SLAM) algorithm. The experiment is done with monocular camera mounted on mobile vehicle. In this paper, final map generated by proposed method is compared with aerial view data. The results show that the proposed method can generate reliable map that is comparable to real roads even only the low-cost sensor is used.

Road Lane Semantic Segmentation for High Definition Map

Unlike high-end three-dimensional (3-D) scanners with more than 16 layers which are mainly used in academia, low-end 3-D scanners with a few layers are being developed by sensor makers for installation in commercial advanced driver assistance system. The output of a low-end 3-D scanner is completely different from that of a full 3-D scanner and it is rather similar to the output of a 2-D scanner with a single layer. In this paper, a new framework for moving object detection and subsequent tracking using a low-end 3-D scanner with four layers is proposed. The proposed method uses the contours of the objects to obtain a robust association between a detection and a tracking. The proposed method comprises five steps: preprocessing, contour extraction, hypothesis generation, pruning, and moving object detection. In the preprocessing step, outliers, such as the ground or backlights from preceding vehicles, are removed and the scanned points are decomposed into segments, each of which corresponds to a single object. In the track hypothesis generation step, each segment is associated with an existing track maintained over multiple scans. The association method developed here uses the contour shape of the segments and is motivated by the linear programming and dynamic time warping. In the track hypothesis pruning step, unlikely tracks are removed from the hypothesis trees based on the proposed hypothesis scores. In the last step, moving objects are detected based on the track velocity. The proposed method is applied to four challenging real-world scenarios, and its validity is demonstrated via experimentation.

A New Contour-Based Approach to Moving Object Detection and Tracking Using a Low-End Three-Dimensional Laser Scanner

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

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Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

Vehicle bounding box tracking (VBBT) is a new problem that is becoming increasingly important in autonomous driving. It is defined as a problem in which not only the position but also the size of a target vehicle is estimated using a sensor. In this paper, novel VBBT using a low-end three-dimensional (3D) laser scanner is proposed. Compared to previous methods, the proposed VBBT has three distinctions: (1) the center of a rectangular vehicle is defined as its position, and the motion model that uses the center of the vehicle as the state is developed; (2) a new measurement model is proposed that models the measured size of the target vehicle as a sample from a uniform distribution; and (3) a Bayesian filter for the proposed motion and measurement model is developed and it is named as the Pareto Kalman filter (PKF). Finally, the proposed method is applied to six scenarios, and its validity is demonstrated through experimentation.

Jhonghyun An

Papers

A Lane-Level Road Marking Map Using a Monocular Camera

Road Lane Semantic Segmentation for High Definition Map

A New Contour-Based Approach to Moving Object Detection and Tracking Using a Low-End Three-Dimensional Laser Scanner

Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner.

Novel Vehicle Bounding Box Tracking Using a Low-End 3D Laser Scanner