Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver's risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver's perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle's Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s 2 and 1.76 m/s 2 , respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle's driver's safety.interval from 3 m to 25 m, from 25 m to 45 m, and from 45 m to 70 m. Wakasugi [20] recommended a two-level TTC threshold of 3 s and 5 s, respectively. However, the computed TTC threshold would be easily influenced by the relative speed and distance between the rear vehicle and the subject vehicle.The remainder of the paper is organized as follows. Related works on lane-change decision models and safety indicator thresholds are introduced in Section 2. Section 3 introduces the naturalistic lane-change trial and extreme moment trials. Section 4 presents the lane-change decision model and the calibration parameters of the model. The two-level threshold based on the proposed model is determined in Section 5. Finally, a discussion and conclusion are presented in Section 6. MethodOn-road experiment is the main research method used in this paper, and the experiments include the naturalistic lane-change trial and extreme moment trials. The purpose of the naturalistic trial is to calibrate the parameters of the proposed lane-change decision model. The extreme moment trials are used to accurately capture the variation of driver cognition characteristics of lane-change safety. In this section, we willintroduce the required equipment, participants, test route and procedures for the experiments in detail. ApparatusThe test vehicle is depicted in Figure 1. The test vehicle used in our experiments was a 2008 Volkswagen Touran, equipped with a Lane Mark Recognition system (Mobileye C2-170, made by Mobileye Company, Jerusalem, Isra...

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles.

There have a large number of pedestrian-vehicle accidents on the pedestrian crossing area in China every year, causing huge loss of life and property. In view of different road conditions, it's crucial to establish a more accurate crossing intention recognition model to improve the safety of pedestrians. In this work, a pedestrian crossing area was chosen. Due to construction reasons, two road conditions appeared in the same crossing area at different periods, namely a condition with a zebra crossing and that without a zebra crossing. We compared pedestrian crossing intention parameters under two road conditions in the same crossing area. The results found that there was a great difference in the characterization parameters of pedestrian crossing intention when the site with and without a zebra crossing. Additionally, a more comprehensive crossing intention characteristic parameters set was established. The characteristic parameters were pedestrian speed, the distance between vehicle and crossing area, time to collision (TTC), and safe vehicle deceleration (SVD), pedestrian age, pedestrian gender, group, respectively. The pedestrian intention recognition model for the site with a and without a zebra crossing were established by long short-term memory network integrated with the attention mechanism (AT-LSTM). When the model recognized pedestrian crossing intention 0.6 seconds in advance, the recognition accuracies were 93.05% and 93.89% respectively. The research results are of great significance for improving the safety of autonomous vehicles in the future, and there are also important to improve pedestrian safety.

Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians’ safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles.

The recognition of the preceding vehicle lane‐changing manoeuvre (LCM) is essential for improving the rationality and safety of the decision‐making of driverless vehicles. However, traditional recognition researches for preceding vehicle LCM are generally characterized by problems such as low recognition accuracy and poor research foundation. In response to these problems, this paper carried out naturalistic driving study (NDS) on the highway, from which a large amount of on‐road data consisting of lane‐keeping (LK) and lane‐changing left and right (LCL, LCR) manoeuvres were collected. A stacking‐based ensemble learning method for the recognition of the LCM of the preceding vehicle, which integrates the random forest, support vector machine, long and short‐term memory network (LSTM), and bi‐directional LSTM based on attention mechanism (AT‐Bi‐LSTM) algorithms, is proposed. Compared with traditional machine learning methods, the proposed method exhibits greater advantages in terms of its recognition accuracy. It is particularly of note that the recognition accuracy of the model at 0.4 s and 0.8 s after the LCM reached 90.77% and 95.54%, respectively. The study reported is of great significance for the construction of more intelligent vehicle‐vehicle collaboration and the promotion of the industrial applications of intelligent vehicle technology.

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot’s physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot’s intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot’s micro-behavior evaluation system.

One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.

Urban trunk road system undertakes the main traffic trip, and congestion occurs frequently in rush hours. In order to clearly describe the propagation process of traffic waves in signalized intersections, and then optimize phase difference. This article proposes a kinematic model for the traffic wave based on the physical mechanism of car-following and the kinematic characteristics of the traffic wave propagation. The actual road traffic monitoring data was extracted from the vehicle-infrastructure cooperative system and vehicle internal communication system. Then we obtained the values of the stop-and-start wave velocity. Compared with the measured data, the results showed that the calculation of the wave velocity of the traffic wave model had a relative error of up to 5% vs the measured data, confirming the validity of the model. Through the analysis of the model, we obtained the difference in the effects on traffic wave velocity of the vehicle speed and the space headway. Our findings provide a theoretical basis for coordinated control and management of urban trunk road traffic and phase difference optimization of signalized intersections. Meanwhile, the research results