Equipment Condition for Zebra Crossing Night-Time Safety Performance in Latvia

According to road traffic accident (hereinafter referred to as RTA) statistics, the vulnerable road users are pedestrians in Latvia. The aim of this study is to investigate and analyse technical equipment used on non-signalled pedestrian crossings (zebra crossings) in Latvia and to make suggestions for measures that would increase road traffic safety on zebra crossings. RTAs involving collisions with pedestrians were filtered from the Ministry of the Interior database for a three-year period from 2016 to 2018. Thirty-two zebra crossings with a higher number of accidents with pedestrians were observed on the spot during the daylight and at night in several cities of Latvia. The main emphasis during the observation was placed on traffic signs and zebra road marking performance. Pedestrian crossings were observed from car driver’s view by taking photographs during day-time and night-time observations. Most attention was paid to road sign and road marking visibility from driver’s seat position. Retroreflection coefficient R’ was measured for each pedestrian crossing road sign. It was found that the condition and performance of traffic organisation equipment were not maintained on a regular basis and the life cycle of some traffic signs had well expired. Many road signs do not comply with minimum requirements, and road markings have weak visibility during wet weather conditions. It is recommended to improve visibility of pedestrian crossings from driver’s view in the urban areas by increasing rain vision for road markings and higher retroreflection class for traffic signs.

Spears and shields: attacking and defending deep model co-inference in vehicular crowdsensing networks

Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge co-inference paradigm to improve the inference efficiency in the VCS network. Specifically, the vehicular device and the edge server keep a part of the deep model separately, but work together to perform the inference through sharing intermediate results. Although vehicles keep the raw data locally, privacy issues still exist once attackers obtain the shared intermediate results and recover the raw data in some way. In this paper, we validate the possibility by conducting a systematic study on the privacy attack and defense in the co-inference of VCS network. The main contributions are threefold: (1) We take the road sign classification task as an example to demonstrate how an attacker reconstructs the raw data without any knowledge of deep models. (2) We propose a model-perturbation defense to defend against such attacks by injecting some random Laplace noise into the deep model. A theoretical analysis is given to show that the proposed defense mechanism achieves $$\epsilon$$ ϵ -differential privacy. (3) We further propose a Stackelberg game-based incentive mechanism to attract the vehicles to participate in the co-inference by compensating their privacy loss in a satisfactory way. The simulation results show that our proposed defense mechanism can significantly reduce the effects of the attacks and the proposed incentive mechanism is very effective.

Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.

Integration of Image-Based Fog Detection with Autonomous Decision System for Intelligent Road Sign

The paper presents the description of the decision system implemented for Intelligent Road Signs. It focuses on the implementation of the novel air transparency analysis system and its integration with the rule system and the speed control infrastructure. Moreover, there are presented issues of making decisions about the content displayed in the case of autonomous and cooperating signs. To reflect more closely on real-life situations, it is assumed that the content presented by the IRS changes dynamically, depending on the road traffic and weather parameters. The IRS system operation was presented using fog detection as an example.