Search Results

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.

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.

The intersection is recognized as the most dangerous area because of the restricted road structures and indeterminate traffic regulations. Therefore, according to the Vehicle-to-everything (V2X) communication, Intelligent Transportation Systems (ITS), and Digital Twin data, we present a potential energy field method to establish the general characteristics of intersection traffic safety, evaluate the safety situation of intersection and assist intersection traffic participants in passing through the intersection safer and more efficient. The resulting potential energy field method is established by the contour line of traffic participants' potential energy, which is constructed as a superposition of disparate energies, such as boundary potential energy, body potential energy, and velocity potential energy. The intersection traffic safety is evaluated by the potential energy field characteristic of simultaneous intersection traffic participants.

In this paper, the principles, advantages and disadvantages of the main technology of variable strength design of automobile B-pillar Based on the finite element simulation technology, the local stress variable strength design effect of Automobile B-pillar structure is simulated, compared and evaluated. The simulation results show that with the same mechanical properties, the overall lightweight degree of B-pillar structure with variable strength design can be reduced by about 8.9%. With the expansion of the strengthening area of variable strength design of parts, the degree of lightweight of parts can be further improved. It can be seen that the local stress variable strength design method provides a new technical option for the lightweight design of automobile parts.

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.

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 this paper, a potential risk assessment algorithm is proposed. The obvious risk assessment measure is defined as time to collision (TTC), whereas the potential risk measure is defined as the time before the host vehicle has to decelerate to avoid a rear-end collision assuming that the target vehicle brakes, i.e. time margin (TM). The driving behavior of the human driver in the dangerous car following scenario is studied by using the naturalistic driving data collected by video drive record (VDR), which include 78 real dangerous car following dangerous scenarios. A potential risk assessment algorithm was constructed using TM and the dangerous car following scenarios. Firstly, the braking starting time during dangerous car following is identified. Next, the TM at brake starting time of the 78 dangerous car following scenarios is analyzed. In the last, the thresholds of the potential risk levels are achieved.

To overcome some drawbacks of using AHSS (Advanced High Strength Steel) in vehicle weight reduction, like brittleness, spot weld HAZ (Heat Affected Zone) softening and high cost, a new ridgeline strengthening technology was introduced and applied to the thin-walled structure in this paper. The energy absorption mechanism of thin-walled box structure with selective strengthened ridgelines under axial compressing load was discussed in first section. After this, the formulas of mean crushing force and corresponding energy absorption for square tube were theoretically discussed. To demonstrate prediction capabilities of formulas, a set of FE simulations of square tubes were conducted. Simulation results show that energy absorption capacity of square tube under quasi-static axial crushing load is dramatically improved by selectively strengthening their ridgelines.

To overcome some drawbacks of using UHSS (Ultra High Strength Steel) in vehicle weight reduction, like spot weld HAZ (Heat Affected Zone) softening, hard machining and brittleness, a new solution of ultra-high stress strengthening was proposed and applied to the ridgelines of thin-walled structures in this paper. Firstly, stress distribution characteristics, the laws of stress variation and the compressed plate buckling process of the rectangular thin-walled beam under compressive and bending load were analyzed in elastic plastic stage by theory and Finite Element (FE) simulation. Secondly, based on elastic plastic buckling theory of the compressed plate and stress distribution similarity of the buckling process of the thin-walled box structure, three factors influencing the ultimate resistance enhancement of thin-walled hat section beam were found, and the rationality and accuracy of cross section ultimate resistance prediction formulas were also verified by FE simulation.

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%.