Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System

Vehicular ad-hoc network (VANET) is constructed of networks using direct radio communication among moving vehicles without fixed infrastructure. Network topologies change frequently due to high mobility and communication disconnections particularly occur due to interruptions of message propagation caused by obstacles such as buildings in urban traffic environment. Therefore, a VANET routing protocol approach to distributing messages among highly mobile vehicles in urban environment is needed. We suggest a scheme to improve the transmission performance at intersections using an approach to the distance-based broadcasting which utilizes the vehicle's position. It determines the stable route decision using the relative velocity among vehicles and zone-based priorities in the intersection. We analyze the performance of schemes under simulating environment implemented with the Manhattan model. The results show that the performance is significantly improved compared with the previous study when using the suggested scheme in the urban traffic environment.

Along with the fast developments in VANET, a large number of attractive applications have emerged, which make routing of data in VANETs a great challenging task, especially in the complex urban traffic environment. The urban environment place restrictions on the packets that they should better be relayed along the road. Moreover, the increasing number of applications intensifies the traffic load condition of the whole network. However, most of the current routing protocols do not take into account the geographic information and traffic load together. In this article, we present a novel routing strategy based on using Geographic and Traffic Load information for VANET in urban traffic environment, GTLR (Geographic and Traffic Load Based Routing). In addition, we compare this new strategy with GPSR and GSR by means of delivering simulation in realistic environment, and the simulation results prove that GTLR outperforms them in terms of packet delivery ratio, and average packet end-to-end delay.

To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.

The development of unmanned logistics vehicles is the general trend, but the right of road for this type of vehicle has not been clarified in China so far, which is not conducive to the healthy development of the unmanned delivery vehicle industry. This paper compares the key parameters of unmanned delivery vehicles in the urban traffic environment and the N1 type vehicles specified by the existing national standards on outer size, performance, and application scenario in order to provide a reference for the supervision of related industries.

As the research hotspot of intelligent transportation system (ITS), unmanned ground vehicle (UGV) with high intelligence is able to reduce the probability of traffic accidents and traffic congestion, and improve the efficiency of urban transportation system. Environmental perception is one of the key research topics in the research of key technologies of UGV. The campus transportation environment, as a simple version of the urban transportation environment, needs to perceive as much traffic information as the urban traffic environment. At the same time, the geographic information system (GIS) has capabilities of strong geographical information storage and management, which can play a critical role in the environmental perception of UGV. In this paper, we study the autonomous driving of unmanned ground vehicle in the campus traffic environment, and the construction method of GIS database is proposed for the perception demand of UGV in the traffic environment of campus. Then, according to the GIS database, autonomous navigation control of UGV is carried out to verify its applicability and effectiveness. After testing, the GIS database proposed in this paper can assist the autonomous driving of UGV well and complete the driving tasks in the campus environment.

The deposition of ambient submicron particles in the different parts of the human respiratory tract (HRT) was, for the first time, estimated for males and females from different age classes (children-adults-seniors) of urban population in the city of Thessaloniki, northern Greece, during the cold and the warm period of the year. Outdoor daily and hourly particle number doses in the different regions of the HRT, i.e., the extra-thoracic (ET), tracheobronchial (TB), and the acinar (AC) regions, were calculated by employing the Multiple-Path Particle Dosimetry (MPPD) model. Because of the absence of information being available for the hygroscopic properties of particles, three different particle hygroscopicity scenarios were considered: (i) non-hygroscopic (i.e., raw model estimations), (ii) nearly hydrophobic, and (iii) hygroscopic particles. When hygroscopic properties were considered, we found a remarkable reduction (up to ~ 55%) in the estimated total particle number doses in comparison to the non-hygroscopic particle scenario. Furthermore, we found that the size distribution pattern of the particle doses within the different parts of the HRT was strongly affected by particles' hygroscopic properties with the non-hygroscopic particle scenario significantly overestimating the particle doses in the sub-100-nm range, while underestimating the doses of larger particles. On the contrary, the deposition density appeared to be negligibly affected by the particles' hygroscopic properties, implying the existence of a possible threshold in the number of particles deposited per airway surface area. Similarly, the lobar particle number deposition fraction was unaffected by the hygroscopic properties of particles, as well as the ambient particle size distribution and the individuals' physiological parameters. The total particle number deposition doses estimated here are within the range of the corresponding values reported for other urban environments. It is hoped that our findings could contribute to better understanding of submicron particle exposure and add to the development of more sufficient methods to evaluate the related health impacts.

In this paper, we propose an accurate and real-time positioning method for intelligent road vehicles in urban environments. The proposed method uses a robust lane marking detection algorithm, as well as an efficient shape registration algorithm between the detected lane markings and a GPS based road shape prior, to improve the robustness and accuracy of global localization of a road vehicle. We exploit both the state-of-the-art technologies of visual localization based on lane marking detection and the wide availability of Global Positioning System (GPS) based localization. We show that by formulating the positioning problem in a relative sense, we can estimate the vehicle localization in real-time and bound its absolute error in centimeter-level by a cross validation scheme. The validation scheme integrates the vision based lane marking detection with the shape registration, and improves the performance of the overall localization system. The GPS localization can be refined by using lane marking detection when the GPS suffers from frequent satellite signal masking or blockage, while lane marking detection is validated and completing by the GPS based road shape prior when it does not work well in adverse weather conditions or with poor lane signature. We extensively evaluate the proposed method with a single forward-looking camera mounted on an autonomous vehicle which travels at 60km/h through several urban street scenes.

Integrated decision-making methodology based on reinforcement learning and imitation learning for automated commercial vehicles in the urban traffic environment

Intelligent surveillance aims at providing artificial systems in order to monitor and improve the security of public and private spaces. Since these environments are complex and the information is distributed through them, agent-based solutions represent a good approach when monitoring moving objects. This paper describes how an existing agent platform has been adopted and used to carry out intelligent surveillance. Within this context, agents implement a behavior-based model that is flexible enough to deal with the challenges that the surveillance tasks pose. The experimental results show how this agent-based approach can contribute to understand events in urban traffic environments.

Pedestrian behavior prediction is essential to enable safe and efficient driving of intelligent vehicles on urban traffic environment. This article presents a novel framework for pedestrian trajectory prediction, which integrates Dynamic Bayesian network and Sequence-to-Sequence model through an adaptive online weighting method. Dynamic Bayesian network utilizes environmental features and kinematic information to infer the pedestrian's motion intentions through probabilistic reasoning. Sequence-to-Sequence model views trajectory predictions as sequence generation tasks, in which the future trajectories are generated relying on the observed trajectories. A real-world pedestrian motion dataset is employed for model validations and it is also enlarged through data augmentation techniques to enable training of data-driven approaches. We compare our model with several typical baselines methods and results show that our model outperforms those baselines. The average error and the final destination error with one-second prediction are 0.04m and 0.10m in crossing scenarios, and 0.06m and 0.17m in stopping scenarios, respectively. The study expects to provide guidelines for the decision-making of intelligent vehicles in order to protect vulnerable road users.

Defensive or competitive Autonomous Vehicles: Which one interacts safely and efficiently with pedestrians?

Single-Camera Video Surveillance now mainly taken the passive monitoring, video surveillance covers small area, the video information is not sufficient without the joint tracking capability. Poor performance is mainly due to unintelligence and low real-time. Distributed information transmission network architecture, the Paper provides a distributed smart multi-eyes camera network, which presents not only the schemes but also its concrete realization. Using the smart camera network with good self-organization in time, the target would be captured and tracked in good manner and the running track for the target can be restored and recovered anytime. It can play an important role in urban traffic environment and video monitoring in public places, which carries a wide range of social and economic benefits.

The ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations.

This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple view-based detectors: frontal, rear, and right/left side view. For each view detector, a linear Support Vector Machine (SVM) is used for object classification in combination with Histograms of Oriented Gradients (HOG) which is one of the most discriminative features. Furthermore, a real-time enhancement for the detection process is implemented using the Integral Histogram method and a coarse-to-fine cascade approach. Tracking phase is performed by a multiple patch-based Lucas-Kanade tracker. We first run the Harris corner detector over the bounding box which is the result of our detector. Each of the corner points can be a good feature to track and, in consequence, becomes a template of each instance of multiple Lucas-Kanade trackers. To manage the set of patches efficiently, a novel method based on spectral clustering algorithm is proposed. Quantitative experiments have been conducted to show the effectiveness of each component of the proposed framework.

Safety and energy are two key issues to affect the development of automotive industry. For the safety issue, the vehicle active collision avoidance system is developing gradually from a high-speed adaptive cruise control (ACC) to the current low-speed stop and go (SG), and the future research topic is the ACC system at full-speed, namely, the advanced ACC (AACC) system. The AACC system is an automatic driver assistance system, in which the driver's behavior and the complex traffic environment ranging are taken into account from high-speed to low-speed. By combining the function of the high-speed ACC and low-speed SG, the AACC system can regulate the relative distance and the relative velocity adaptively between two vehicles according to the driving condition and the external traffic environment. Therefore, not only can the driver stress and the energy consumption caused by the frequent manipulation and the traffic congestion both be reduced effectively at the urban traffic environment, but also the traffic flow and the vehicle safety will be improved on the highway. Taking the actual traffic environment into account, the velocity of vehicle changes regularly in a wide range and even frequently under SG conditions. It is also subject to various external resistances, such as the road grade, mass, as well as the corresponding impact from the rolling resistance. Therefore, the behaviors of some main components within the power transmission show strong nonlinearity, for instance, the engine operating characteristics, automatic transmission switching logic and the torque converter capacity factor. In addition, the relative distance and the relative velocity of the inter-vehicles are also interfered by the frequent acceleration/deceleration of the leading vehicle. As a result, the performance of the longitudinal vehicle full-speed cruise system (LFS) represents strong nonlinearity and coupling dynamics under the impact of the external disturbance and the internal uncertainty. For such a complex dynamic system, many effective research works have been presented. J. K. Hedrick et al. proposed an upper+lower layered control algorithm concentrating on the high-speed ACC system, which was verified through a platoon cruise control system composed of multiple vehicles [1-3]. K. Yi et al. applied some linear control methods, likes linear quadratic (LQ) and proportional–integral–derivative (PID), to design the upper and lower layer controllers independently for the high-speed ACC system [4]. In ref.[5], Omae designed the model matching control (MMC) vehicle high-speed ACC system based on the H-infinity (Hinf) robust control method. To achieve a tracking control between