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To promote the development of automated vehicles (AVs), large scale of field operational tests (FOTs) were carried out around the world. Applications of naturalistic driving data should base on correlative scenarios. However, most of the existing scenario typologies, aiming at advanced driving assistance system (ADAS) and extracting discontinuous fragments from driving process, are not suitable for AVs, which need to complete continuous driving tasks. In this paper, a systematic scenario-typology consisting of four layers (from top to bottom: trip, cluster, segment and process) was first proposed. A trip refers to the whole duration from starting at initial parking space to parking at final one. The basic units ‘Process’, during which the vehicle fulfils only one driving task, are classified into parking process, long-, middle- and short-time-driving-processes. A segment consists of two neighboring long-time-driving processes and a middle or/and short one between them.

Low-speed electric vehicles (LSEV) are very popular in the less-developed area in China, and the industrial scale of LSEV is gradually expanding due to their great fit to the short-distance urban and rural travel and low purchase cost. However, the R&D strength and design investment of LSEV enterprises are uneven, and there are generally problems of unreasonable body structure and poor collision safety performance among LSEVs. In the transition period when the national mandatory standards are being developed, in order to make the LSEV enterprises complete the technology upgrade, this paper will build a FEM model based on a certain type of mass production vehicle and conduct collision simulation test to analyze the weak structure of the original structure and proposed a structural optimization.

Analysis of driver brake behavior parameters of cut-in scenarios is conducted based on naturalistic driving data and accident collision data. The characters of different critical level cut-in cases (normal case, conflict case and collision case) can be obtained by the Time headway (THW) and relative velocity parameter when driver brake initiation. The representative cases, which are selected according to the risk evaluation method, are chosen to evaluate driver’s timing of brake initiation. Using logistical regression method, the cut-in boundary condition model is established with THW and relative velocity parameters. Based on the China Field Operation Test database and China in Depth Accident Study database, the comfortable and safety boundary conditions of cut-in scenarios are established, which is used to optimization the longitudinal control systems of Chinese intelligent connected vehicles.

Based on the emergency lane change cases extracted from the China naturalistic driving data, the driving steering behavior divides into three phases: collision avoidance, lateral movement and steering stabilization. Using the steering primitive fitting by Gaussian function, the distribution of the duration time, the relationship between steering wheel rate and deflection were analyzed in three phases. It is shown that the steering behavior essentially is composed of steering primitives during the emergency lane-change. However, the combination of the steering primitives is different according to the specific steering constraints in three phases. In the collision avoidance phase, a single steering primitive with high peak is used for the fast steering; in the lateral movement and stabilization phase, a combination of two or even more steering primitives is built to a more accurate steering.

A driver steering model for emergency lane change based on the China naturalistic driving data is proposed in this paper. The steering characteristic of three phases is analyzed. Using the steering primitive fitting by Gaussian function, the steering behaviors in collision avoidance and lateral movement phases can be described, and the stabilization steering principle of yaw rate null is found. Based on the steering characteristic, the near and far aim point used in steering phases is analyzed. Using the near and far aim point correction model, a driver steering model for emergency lane change is established. The research results show that the driver emergency steering model proposed in this paper performs well when explaining realistic steering behavior, and this model can be used in developing the ADAS system.

For the personification of automotive vehicle function performance under common traffic scenarios, analysis of human driver behavior is necessary. Based on China Field Operational Test (China-FOT) database of China Natural Driving Study project, this paper studies the driver's response in the common cut-in scenario. A total of 266 cut-in cases are selected by manual interception of driving recorder video. The relevant traffic environment characteristics are also extracted from video, including light conditions, road conditions, scale and lateral position of cut-in vehicle, etc. Dynamic information is decoded form CAN, such as speed, acceleration and so on. Then image processing results, such as relative speed and distance of cut-in and subject vehicles, are calculated. Statistical results based on above information show the response type and distribution of human driver: the behavior of keeping lane is 96.24%, in which the ratio of braking response is 51.13%.

Due to the differences in traffic situations and traffic safety laws, standards for extraction of critical driving scenarios (CDSs) vary from different countries and areas around the world. To maintain the characteristic variables under the Chinese typical CDSs, this paper uses the three-layer detection method to extract and detect CDSs in the Natural Driving Data from China-FOT project which executing under the real traffic situation in China. The first layer of detection is mainly based on the feature distributions which deviate from normal driving situations. These distributions associated with speed and longitudinal acceleration/lateral acceleration/yaw rate also quantify the critical levels classification.

In order to improve the efficiency of safety performance test for intelligent vehicles and construct the test case set quickly, critical scenarios based on graded hazard disposal model of human drivers are proposed, which can be used for extraction of test cases for safety performance. Based on the natural driving data in China Field Operational Test (China-FOT), the four-stage collision avoidance process of human drivers is obtained, including steady driving stage, risk judgment stage, collision reaction stage and collision avoidance stage. And there are two human driver states: general state and alert state. Then the graded hazard disposal model of human drivers is constructed.

Cut-in scenario is common in traffic and has potential collision risk. Human driver can detect other vehicle’s cut-in intention and take appropriate maneuvers to reduce collision risk. However, autonomous driving systems don’t have as good performance as human driver. Hence a deeper understanding on driving behavior is necessary. How to make decisions like human driver is an important problem for automated vehicles. In this paper, a method is proposed to classify the dangerous cut-in situations and normal ones. Dangerous cases were extracted automatically from naturalistic driving database using specific detection criteria. Among those cases, 70 valid dangerous cut-in cases were selected manually. The largest deceleration of subject vehicle is over 4 m/s2. Besides, 249 normal cut-in cases were extracted by going through video data of 2000km traveled distance. In normal driving cases, subject vehicle may brake or keep accelerating and the largest deceleration was less than 3 m/s2.

In China there are many mixed driving roads which cause a lot of safety problems between vehicles and pedalcyclists. Research on driver behavior under risk scenarios with pedalcyclist is relatively few. In this paper driver brake parameters under naturalistic driving are studied and pedalcyclists include bicyclist, tricyclist, electric bicyclist and motorcyclist. Brake reaction time and maximum brake jerk are used to evaluate driver brake reaction speed. Average deceleration is used to evaluate the effect of driver brake operation. Maximum deceleration is used to evaluate driver braking ability. Driver behaviors collected in China are classified and risk scenarios with pedalcyclist are obtained. Driver brake parameters are extracted and statistical characteristics of driver brake parameters are obtained. Influence factors are analyzed with univariate ANOVA and regression analysis.

In China Cut-in scenarios are quite common on both highway and urban road with heavy traffic. They have a potential risk of rear-end collision. When facing a cutting in vehicle, driver tends to brake in most case to reduce collision risk. The timing and dynamic characteristics of brake maneuver are indicators of driver subjective risk perception. Time to collision (TTC) and Time Headway (THW) demonstrate objective risk. This paper aims at building a model quantitatively revealing the relationship between drivers’ subjective risk perception and objective risk. A total of 66 valid critical Cut-in cases was extracted from China-FOT, which has a travel distance of about 130 thousand miles. It is found that under Cut-in scenarios, driver tended to brake when the cutting in vehicle right crossing line. This time point was defined as initial brake time. Brake strength and brake speed were taken to describe brake maneuver.

The intersection is widely recognized as the most dangerous section because of the complex traffic environment, frequent traffic signals, and traffic violations. Therefore, Vehicle-to-everything (V2X) communication technology is established to improve road safety and traffic efficiency for the above problems. According to the V2X application, we present a potential energy model to establish the general characteristics of vehicle group safety, evaluate the safety situation of intersection and assist Intelligent and Connected Vehicle (ICV) in driving through intersection more safety and efficient. According to the research on traffic follow, the potential field theory was used to represent the potential energy. The resulting potential energy model is constructed as a superposition of disparate energies, which are boundary potential energy, vehicle potential energy, and velocity potential energy.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

Cut-in scenarios are common and of potential risk in China but Advanced Driver Assistant System (ADAS) doesn’t work well under such scenarios. In order to improve the acceptance of ADAS, its reactions to Cut-in scenarios should meet driver’s driving habits and expectancy. Brake is considered as an express of risk and brake tendency in normal Cut-in situations needs more investigation. Under critical Cut-in scenarios, driver tends to brake hard to eliminate collision risk when cutting in vehicle right crossing lane. However, under less critical Cut-in scenarios, namely normal Cut-in scenarios, driver brakes in some cases and takes no brake maneuver in others. The time when driver initiated to brake was defined as key time. If driver had no brake maneuver, the time when cutting-in vehicle right crossed lane was defined as key time. This paper focuses on driver’s brake tendency at key time under normal Cut-in situations.

Based on FOT data of a Chinese automobile company, this paper aims to study the practical role of lane departure warning system. The data of this automobile company collects a total of 32.29 hours of test data, including vehicle control, lane line and other relevant information, FOT data included both test groups and contrast groups. This paper designs research questions for the development purpose of LDW system: whether the LDW system can affect driver behavior or vehicle performance to improve road safety. To solve this problem, a hypothesis is proposed: due to the role of LDW system, in the test group and contrast group, the driving safety of the test group is higher than that of the benchmark group. According to the research hypothesis, three analysis indexes of the test are determined and defined: the number of road deviation, the time of road deviation and the maximum distance of road deviation, which are collectively referred to as safety and benefit indexes.

With the increasing intelligence of human society, people's demand for human-computer interaction is also increasing. As an important communication medium for human to express information, gesture has always been an important topic in human-computer interaction. Using gesture recognition technology in the vehicle environment can reduce the operation difficulty during driving, reduce the possibility of driver distraction, and greatly improve driving safety and driving experience. Millimeter wave radar can effectively protect the privacy in the car from being leaked, and can still work normally in the dark interior environment. Moreover, with the development of millimeter wave technology from 24g to 60g and 77g, the improvement of its resolution further improves its ability to detect small displacement. Therefore, the gesture recognition technology using millimeter wave radar has been developed.

On account of the insufficient lane-changing scenario test cases and the inability to conduct graded evaluation testing in current autonomous driving system field testing, this paper proposed an approach that combined data-driven and knowledge-driven methods to extract lane-changing test concrete scenarios with graded risk levels for field testing. Firstly, an analysis of the potentially hazardous areas in lane-changing scenarios was conducted to derive key functional lane-changing scenarios. Three typical key functional lane-changing scenarios were selected, namely, lane-changing with a preceding vehicle braking, lane-changing with a preceding vehicle in the same direction, and lane-changing with a rear cruising vehicle in the adjacent lane, and their corresponding safety goals were respectively analyzed. Secondly, the GAMAB criterion was introduced as an evaluation standard for autonomous driving systems.