A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

基于物联网的嵌入式酒驾智能辨识系统设计

针对目前因酒驾造成的交通事故频发问题,提出了一种基于物联网的车载酒驾自动检测系统。该系统利用物联网技术,以STC12C5A16AD单片机为核心,通过4路MQ-3传感器来检测车内驾驶员是否酒驾。当系统检测到酒精浓度超标时,系统发出声光报警并禁止汽车发动,同时通过GPRS模块进行车辆定位,并将信息发送给家人和当地交通部门。实验结果表明,该系统检测灵敏度和准确率高,能有效预防酒后驾车,具有很好的实用价值。     

Determining driver phone use leveraging smartphone sensors

Driver distraction by mobile phones has been a huge threat that leads to unnecessary accidents and human casualties, especially in hazardous road conditions. In this paper, we address a fundamental but critical issue of phone use during the driver behind the wheel. We propose, design and implement SafeDrive which achieves the goal of automatically determining driver phone use leveraging built-in smartphone sensors sensing driving conditions. We explore GPS and accelerometer sensors on smartphones to collect data, which can sufficiently capture driving conditions. With inputs of these data, we provide an accurate driving condition classification algorithm, that classifies driving conditions into five categories. Based on the classified driving conditions, SafeDrive makes a flexible control of driver phone use. We excessively evaluate the classification accuracy of our SafeDrive in local, highway, traffic jam, and complex conditions, respectively, and the results demonstrate that it can achieve up to 87 % classification accuracy in complex conditions.     

The prediction of car driver size and position to enhance safety in crashes

The positions which car drivers adopt when driving will depend on their anthropometric characteristics, the range and type of adjustment available from the vehicle package and their preferred driving posture. The design and testing of systems to protect occupants in car crashes assumes that the size and position of the driver is ‘normal’ or ‘average’, although there is some accommodation for adjustability. If, however, the occupant protection system had information on the driver's chosen seat position, on whether the driver was particularly large or small and on whether the driver was sitting close to or further from the steering wheel, in a crash the system could tailor its performance and enhance the protection offered. This study investigated whether it was possible to predict the physical characteristics of the driver and the driver's position in relation to the steering wheel, from data that could be collected by sensors in the seat and seat mounting. In order to do this, anthropometric characteristics of drivers and their usual seated position in their own vehicle were measured and analyses were undertaken to identify whether there were any relationships between the driver-related and the vehicle-related measures. The results showed that it was possible to predict drivers' head and chest positions relative to injury-producing features of the vehicle such as the steering wheel (and hence the airbag) and to predict some physical dimensions of drivers.     

Perception,information processing and modeling: Critical stages for autonomous driving applications

Over the last decades, the development of Advanced Driver Assistance Systems (ADAS) has become a critical endeavor to attain different objectives: safety enhancement, mobility improvement, energy optimization and comfort. In order to tackle the first three objectives, a considerable amount of research focusing on autonomous driving have been carried out. Most of these works have been conducted within collaborative research programs involving car manufacturers, OEM and research laboratories around the world. Recent research and development on highly autonomous driving aim to ultimately replace the driver's actions with robotic functions. The first successful steps were dedicated to embedded assistance systems such as speed regulation (ACC), obstacle collision avoidance or mitigation (Automatic Emergency Braking), vehicle stability control (ESC), lane keeping or lane departure avoidance. Partially automated driving will require co-pilot applications (which replace the driver on his all driving tasks) involving a combination of the above methods, algorithms and architectures. Such a system is built with complex, distributed and cooperative architectures requiring strong properties such as reliability and robustness. Such properties must be maintained despite complex and degraded working conditions including adverse weather conditions, fog or dust as perceived by sensors. This paper is an overview on reliability and robustness issues related to sensors processing and perception. Indeed, prior to ensuring a high level of safety in the deployment of autonomous driving applications, it is necessary to guarantee a very high level of quality for the perception mechanisms. Therefore, we will detail these critical perception stages and provide a presentation of usable embedded sensors. Furthermore, in this study of state of the art of recent highly automated systems, some remarks and comments about limits of these systems and potential future research ways will be provided. Moreover, we will also give some advice on how to design a co-pilot application with driver modeling. Finally, we discuss a global architecture for the next generation of co-pilot applications. This architecture is based on the use of recent methods and technologies (AI, Quantify self, IoT …) and takes into account the human factors and driver modeling.     

Collision-avoidance systems PRORETA: Situation analysis and intervention control

After a discussion of passive and active safety systems for automobiles and accident statistics it follows that a further progress in the reduction of accidents can be especially expected by next-generation driver-assistance systems with a sequence of warnings and active interventions. PRORETA is an Industry–University research project with the goal to develop steps towards accident-free driving. The first project considers two vehicles moving in the same direction. The other vehicle is detected by a fusion of LIDAR and camera data providing the system with relative speeds and distance. If the driver does not react to an obstacle on the own lane, the system automatically triggers an emergency braking and/or swerving to avoid a collision. This includes e.g. a fast and precise evasive trajectory control by automatic steering.The second project is dedicated to vehicles moving in opposite directions performing an overtaking maneuver on rural roads. The own vehicle detects the velocities and distances to the preceding and oncoming vehicle by RADAR and lane markings, etc. with a camera. The measured data of the two sensors undergo a sensor fusion with Kalman filters. The overtaking maneuver is predicted by using the measured data of all three vehicles. If an accident-free overtaking is in danger, warnings are given to the driver and if the driver does not react in time a full braking of the own vehicle is triggered such that the driver can turn back behind the overtaken vehicle.The contribution gives a survey on the developed strategies and some basic calculated features and control systems. Measured data are shown and give an impression of the driving experiments on the runway of an air field.     

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.     

Shared control for lane departure prevention based on the safe envelope of steering wheel angle

The ability to prevent lane departure has become an important feature for commercialized vehicles. This paper proposes a shared steering assistance strategy based on a safe envelope of steering wheel angle (SWA). This solves the human-machine conflict issue in lane departure prevention (LDP) system which uses steering control to help the driver keep the vehicle within the correct lane. The system combines a driver steering control model, current vehicle states and vehicle-road deviation. The desired SWAs are calculated when the driver intends to drive along the left or right side of the lane, and then the two angles are used to generate the safe envelope. Next, a driver intention estimator is designed to predict driver’s intended SWA and the assistance control is activated by judging whether the driver intended SWA is go beyond the safe envelope. Finally, a H∞ controller and a disturbance observer are developed to determine the assistance torque. In this way, the SWA is limited to safe values to mitigate lane departure and the controller intervention is minimized. The effectiveness of the proposed method is evaluated via numerical simulation with different driving scenarios and human-in-the-loop experiment on a driving simulator. The obtained results show that this method not only can avoid lane departures effectively, but also ensures a good human-machine cooperative performance.     

A Lane-Level Road Marking Map Using a Monocular Camera

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.      

Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

Abstract

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).     

Response Times for Auditory and Vibrotactile Directional Cues in Different Immersive Displays

ABSTRACT

Using multisensory signals in advanced driver assistance is continuously increasing as a way to increase the attention and reduce the reaction time. It is essential that driver assistance system is capable of providing directional cues to the driver to direct his attention to the sides of the car as usually the focus is in front of the car. These signals could be for blind spot information, navigation, lane departure warning, collision warning, etc. This study investigated the effect of auditory and vibrotactile on directional attention in driver assistance systems. Moreover, two types of immersive displays were used in the driving simulation, namely the Head Mounted Display (HMD) and CAVE display, to study the effect of the type of display on the human performance. Lane Chang Task was used to assess the attention by measuring the response time to directional information. Vibrotactile and Auditory cues induced equal response times, meanwhile, vibrotactile signal was significantly gained higher satisfaction than auditory.     

A systematic study of driver steering behaviour

A sequence of driving tasks has been carried out in a driving simulator. The initial tests represented lane tracking along a serpentine roadway and were employed to verify the operation of the simulator and the ability of a computer algorithm to fit linear driver models to experimental data. A second series of tests involved an obstacle avoidance manoeuvre in both a car and a truck. These latter simulator runs were augmented by field trials in an automobile during which driver eye point-of-regard data were recorded. Eye point-of-regard results from both simulator and field trials were compared and employed in formulating a simple driver model for the obstacle avoidance manoeuvre. The results from a preliminary fitting of this model to the experimental data are reported. It was found that a single linear model of the driver's dynamic characteristics can be used to represent adequately all of the driver response data measured in the present study.     

Announcing Automated Lane Changes: Active Vehicle Roll Motions as Feedback for the Driver

ABSTRACT

As supervisor of the automation system during partially automated driving, it is essential for the driver to have a good awareness of the automation to fulfill this new task sufficiently. Therefore, feedback about intentions of the automation, for example lane changes, is crucial. So far, this feedback is mainly presented visually to the driver. Conversely, in this article, feedback for announcing lane changes is realized via active vehicle roll motions. Several designs are implemented in an automated test vehicle and are evaluated in four different driving scenarios on a test track. Totally, 39 participants rated the vehicle roll motions, for example, regarding the items roll direction, intensity, usefulness, and the predictability of the driving behavior. The results show that active roll motions as feedback for announcing automated lane changes should be clearly perceptible and are considered useful, not misleading, and support the drivers regarding their mode/system awareness.     

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control (MPC) controller driving the vehicle along an optimal safe path. The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.      

Automatic Lane-Level Intersection Map Generation using Low-Channel Roadside LiDAR

A lane-level intersection map is a cornerstone in high-definition (HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor (LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are 96% and 94.12%, respectively. Compared with the satellite-based, MMS-based, and crowdsourcing-based lane mapping methods, the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.      

Method for the analysis of posture and interface pressure of car drivers

Biomechanical study of car driver posture is one of the most referenced aspects for the ergonomic design process of the whole vehicle. The aim of this work is to present a multi-factor method for the analysis of sitting posture and the resulting interactions of the car driver body with the cushion and the backrest. The proposed method, based on the combined use of an optoelectronic system for motion capture and suitable matrices of pressure sensors, has allowed the measurement of a large set of car driver posture parameters and the identification of specific sitting strategies characterising the driving posture, despite the different behaviours of the analysed subjects.     

Development and application of an integrated traffic simulation and multi-driving simulators

Professional virtual reality experiment tools, including driving simulators and traffic simulators, have their strengths and weaknesses. The integration of the two simulators will enhance the ability of both traffic modeling and driving simulation and present a new area of applications. This paper develops, implements, and validates an experimental platform that integrated a traffic simulator with multiple driving simulators (TSMDS). As a connected multi-user framework that allows multiple drivers who are simultaneously handling many driving simulators, it not only allows driver behavior experiments to be more accurate, controlled, and versatile but also simulates special driving behavior or multi-vehicle interactions under more realistic traffic flow environments. To validate the performance of TSMDS, 27 drivers were recruited to attend the lane changing experiments at a recurring on-ramp bottleneck and left-turn experiments at a two-phase signalized intersection in Shanghai. Both experiments required several drivers to drive the TSMDS and fulfill several complicated lane changing/crossing behaviors through their interaction. The results show that both the participants’ response and lane changing/crossing data that were obtained from the experiment are consistent with the field observation, which confirms the validity of the integrated platform.     

简化路况模式下驾驶员情绪模型的研究

驾驶辅助系统中的驾驶员模型较为单一, 没有考虑驾驶员的情绪状态对驾驶策略的影响. 为此, 本文研究了简化路况下驾驶员的情绪模型. 基于OCC (Ortony-clore-collins) 模型、情绪状态自发转移过程的马尔科夫模型和情绪状态刺激转移的隐马尔科夫模型(Hidden Markov model, HMM), 本文提出路况变化和无路况两种情况下的情绪模型, 并对驾驶员的跟驰、切换车道和超车过程中的情绪变化进行了研究. 在自发转移过程中, 结合情绪实时变化的特性, 提出了时变的自发转移过程,而在情绪刺激转移中, 考虑了情感对刺激的记忆效应, 即同种刺激先后对情感影响不同. 讨论了认知情感的变化对驾驶策略的影响. 针对车距、路宽和周围车辆车速对驾驶员的情感影响程度、刺激敏感程度以及特定事件对驾驶员的影响过程, 进行了仿真实验, 预估出驾驶员在特定事件刺激下会采取何种驾驶策略. 并进行了实测数据验证, 实验结果验证了所提出模型的有效性, 为驾驶辅助系统中建立驾驶员模型提供了有借鉴意义的基础理论.     

Applying fuzzy method to vision-based lane detection and departure warning system

As the high growth of population of vehicles, the traffic accidents are becoming more and more serious in recent years. Most occurrences of the car accidents results from the distraction, inattention and driving fatigue of the driver. Hence, in order to avoid the driver being in danger as much as possible. In the lane detection, in order to enhance lane boundary information and to suitable for various light conditions all day, we combine the self-clustering algorithm (SCA), fuzzy C-mean and fuzzy rules to process the spatial information and Canny algorithms to get good edge detection. In the lane departure warning, the system uses instantaneous information from the lane detection to calculate angle relations of the boundaries. The system sends a suitable warning signal to drivers, according to degree different of the departure. These experiments have been successfully evaluated on the PC platform of 3.2-GHz CPU and the average frame rate is up to 14 fps.