Typical Traffic Scenes Research Articles

RRER: A refined registration method based on contrast minimum for event and RGB cameras

AbstractThe precise perception of the surrounding environment in traffic scenes is an important part of an intelligent transportation system. The event camera could provide complementary information to traditional frame‐based cameras, such as high dynamic range, and high time resolution, in the perception of traffic targets. To improve the precision and reliability of perception as well as facilitate lots of RGB camera‐based studies introduced to event cameras directly, a refined registration method for event‐based cameras and RGB cameras on the basis of pixel‐level region segmentation is proposed, to provide a fusion method at pixel level. A total of eight sequences and a dataset containing 260 typical traffic scenes are contained in the experiment dataset, both selected from DSEC, a traffic event‐based dataset. The registered event image shows a better spatial consistency with RGB images visually. Compared to the baseline, the evaluation indicators, such as the performance of the contrast, the proportion of overlapping pixels, and average registration accuracy have been improved. In the traffic object segmentation task, the average boundary displacement error of our method has decreased and the max decline value has reached 79.665%, compared to the boundary displacement error between ground truth and baseline. These results indicate prospective applications in the perception of intelligent transportation systems combined with event and RGB cameras. The traffic dataset with pixel‐level semantic annotations will be provided soon.

3D Vehicle Information Recognition Algorithm of Monocular Camera Based onSelf-Calibration in Traffic Scene

<p indent="0mm">Obtaining 3D information of vehicles as the basis for accurate classification of vehicles has become an increasingly important research direction. However, most of the current traffic monitoring cameras are monocular cameras, which cannot directly obtain 3D information of vehicles like pose and size due to perspective factors. According to the above problem, this paper proposes a 3D vehicle information recognition algorithm of monocular camera based on self-calibration in traffic scene. Firstly, this paper builds up a monocular camera model and a stable single vanishing point calibration model according to the typical traffic scene, and completes camera calibration. Then it uses the YOLO deep learning convolution neural network for 2D vehicle detection. Based on this, it puts forward a diagonal and vanishing point constrained non-linear optimization algorithm, combining with the calibration information to complete 3D vehicle information recognition and the best 3D vehicle detection. Finally, the experiment was carried out on the public dataset called BrnoCompSpeed and in highway traffic scenes, and the results show that the algorithm can effectively complete 3D vehicle information recognition in various traffic scenarios with an average recognition accuracy of more than 90%.

Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data.

Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle’s image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect.

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.

Estimation of Initial Position Using Line Segment Matching in Maps

While navigating in a typical traffic scene, with a drastic drift or sudden jump in its Global Positioning System (GPS) position, the localization based on such an initial position is unable to extract precise overlapping data from the prior map in order to match the current data, thus rendering the localization as unfeasible. In this paper, we first propose a new method to estimate an initial position by matching the infrared reflectivity maps. The maps consist of a highly precise prior map, built with the offline simultaneous localization and mapping (SLAM) technique, and a smooth current map, built with the integral over velocities. Considering the attributes of the maps, we first propose to exploit the stable, rich line segments to match the lidar maps. To evaluate the consistency of the candidate line pairs in both maps, we propose to adopt the local appearance, pairwise geometric attribute and structural likelihood to construct an affinity graph, as well as employ a spectral algorithm to solve the graph efficiently. The initial position is obtained according to the relationship between the vehicle's current position and matched lines. Experiments on the campus with a GPS error of dozens of metres show that our algorithm can provide an accurate initial value with average longitudinal and lateral errors being 1.68m and 1.04m, respectively.

Study on Driver Visual Physiological Characteristics in Urban Traffic

In the integrated traffic environment, human factor is always a main factor of the three elementary factors, besides the vehicle and road factor. The driver physiological and psychological characteristics have an important impact especially on traffic safety in urban road traffic conditions. Some typical traffic scenes in condition of urban road, such as light signal control at intersection, overtaking, and passing, are selected for condition analysis. An eye movement apparatus was used to obtain driver eye closure, blink frequency, and other visual physiological indicators in the traffic conditions of urban road. The regular patterns of driver visual characteristics in the corresponding scenes were analyzed in detail to provide data and theoretical support for the further research on traffic safety of urban environment from the viewpoint of driver psychology and behavior.