Showing papers in "Iet Intelligent Transport Systems in 2014"

Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

Abstract: Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains.

Missing traffic data: comparison of imputation methods

Abstract: Many traffic management and control applications require highly complete and accurate data of traffic flow. However, because of various reasons such as sensor failure or transmission error, it is common that some traffic flow data are lost. As a result, various methods were proposed by using a wide spectrum of techniques to estimate missing traffic data in the last two decades. Generally, these missing data imputation methods can be categorised into three kinds: prediction methods, interpolation methods and statistical learning methods. To assess their performance, these methods are compared from different aspects in this paper, including reconstruction errors, statistical behaviours and running speeds. Results show that statistical learning methods are more effective than the other two kinds of imputation methods when data of a single detector is utilised. Among various methods, the probabilistic principal component analysis (PPCA) yields best performance in all aspects. Numerical tests demonstrate that PPCA can be used to impute data online before making further analysis (e.g. make traffic prediction) and is robust to weather changes.

Architecture for parking management in smart cities

Abstract: Parking is becoming an expensive resource in almost any major city in the world, and its limited availability is a concurrent cause of urban traffic congestion, and air pollution. In old cities, the structure of the public parking space is rigidly organised and often in the form of on-street public parking spots. Unfortunately, these public parking spots cannot be reserved beforehand during the pre-trip phase, and that often lead to a detriment of the quality of urban mobility. Addressing the problem of managing public parking spots is therefore vital to obtain environmentally friendlier and healthier cities. Recent technological progresses in industrial automation, wireless network, sensor communication along with the widespread of high-range smart devices and new rules concerning financial transactions in mobile payment allow the definition of new intelligent frameworks that enable a convenient management of public parking in urban area, which could improve sustainable urban mobility. In such a scenario, the proposed intelligent parking assistant (IPA) architecture aims at overcoming current public parking management solutions. This study discusses the conceptual architecture of IPA and the first prototype-scale simulations of the system.

Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance

Abstract: Activities in the field of automated driving have produced a variety of development tools and methodologies over the past decades. The requirements the systems have to fulfil and thus also the development guidelines are often documented in different kinds of catalogues (use-case catalogues, situation catalogues, scenario catalogues etc.). These catalogues cannot be directly applied for the development of partially and highly automated vehicle guidance concepts like conduct-by-wire (CbW) or H-mode. One reason is that up to now, no consistent terminology known to the authors yet exists for vehicle automation within the community. Moreover, as the aim of the two project groups CbW and H-mode is to make a comprehensive feasibility assessment of cooperative vehicle guidance, all interacting components of the overall system as well as all potential driving conditions a cooperative vehicle guidance system might have to cope with have to be analysed. This article focuses on two aspects. The first is a metaphor-based terminology discussion leading to a proposal for a fundamental ontology. The second aspect is an outlook on the different catalogues that use the new terminology and that have been developed. The methodology introduced here is a fundamental contribution towards simplifying communication and the exchange of findings.

Cooperative vehicle-actuator system: a sequence-based framework of cooperative intersections management

Abstract: With the emergence of cooperative driving, an innovative traffic control concept, Cooperative Intersection Management (CIM), has emerged. In the framework of CIM, vehicles can wirelessly communicate with the intersection controller for negotiating the right-of-way. Each vehicle is dealt with individually by the controller. Thus, the right-of-way can be customised on measurement of vehicles’ state. CIM has a great potential to increase the capacity of intersections. However, this new concept deserves a profound research such as finding an efficient control mechanism that can balance between safety and efficiency issues. This study proposes a sequence-based system, which is referred to as cooperative vehicle-actuator system, to achieve this purpose. The control in this system turns to determine the vehicle passing sequence under safety constraints. Two control strategies under this system are introduced to find the vehicles passing sequence in real time. Microscopic simulations are implemented to verify the performance of the proposed system. Besides, a real intersection test is carried out to prove the feasibility of the system in real world applications.

Video-based traffic data collection system for multiple vehicle types

Abstract: Traffic data of multiple vehicle types are important for pavement design, traffic operations and traffic control. A new video-based traffic data collection system for multiple vehicle types is developed. By tracking and classifying every passing vehicle under mixed traffic conditions, the type and speed of every passing vehicle are recognised. Finally, the flows and mean speeds of multiple vehicle types are output. A colour image-based adaptive background subtraction is proposed to obtain more accurate vehicle objects, and a series of processes like shadow removal and setting road detection region are used to improve the system robustness. In order to improve the accuracy of vehicle counting, the cross-lane vehicles are detected and repeated counting for one vehicle is avoided. In order to reduce the classification errors, the space ratio of the blob and data fusion are used to reduce the classification errors caused by vehicle occlusions. This system was tested under four different weather conditions. The accuracy of vehicle counting was 97.4% and the error of vehicle classification was 8.3%. The correlation coefficient of speeds detected by this system and radar gun was 0.898 and the mean absolute error of speed detection by this system was only 2.3 km/h.

Active collision avoidance system for steering control of autonomous vehicles

Abstract: The study proposes an active collision avoidance system to allow safe lane-changing manoeuvres by self-steering vehicles in the presence of the uncertainties associated with nearby vehicles and the surrounding environment This system integrates estimation of conflict probability, model predictive control and dedicated short-range communications (DSRC) techniques to ensure a collision-free operation To accomplish this, the proposed system uses model predictive control to predict the future positions of vehicles and estimates the conflict probability so as to reduce the risk of collision The system also exploits DSRC techniques to facilitate the gathering of information from nearby vehicles so that potential conflicts can be detected at an earlier stage Autonomous vehicles can thus make adjustments based on the acquired data to avoid collisions in a real communication environment The effectiveness of the method has been verified under experimental conditions The influences of key parameters in the control method are examined

Potential impacts of ecological adaptive cruise control systems on traffic and environment

Abstract: In this contribution, the authors put forward a modelling framework for generic Advanced Driver Assistance Systems (ADAS) based on rolling horizon optimal control and design control algorithms for an Ecological Adaptive Cruise Control (EcoACC) system under this framework. The accelerations of EcoACC vehicles are determined by minimizing some predicted cost, and the optimal control problem is solved using a dynamic programming approach. The proposed algorithm is applied on a single lane ring road to examine the impacts of the EcoACC system employing the Eco-driving strategy comparison with a system employing an Efficient-driving strategy. Simulation results show that the Eco-driving strategy results in smoother vehicle behaviour compared to the driving strategies that only consider travel efficiency (Efficient-driving strategy). At the macroscopic level, the Eco-driving strategy results in a lower speed and lower flow at free traffic conditions, but a higher speed and higher flow at moderate congested conditions compared to the Efficient-driving strategy. From an environment perspective, the Eco-driving strategy results in a lower spatial CO₂ emission rate. However, in the ring-road scenario where the demand is not fixed, the impact of the EcoACC system on total CO₂ emissions is negative at moderate congested conditions, due to the high flow it supports.

Understanding drivers’ perspective on parking guidance information

Abstract: Parking guidance and information (PGI) systems are thought to enable a more efficient control and management of the traffic and the use of the available car park in urban areas. Despite the installation of PGI systems in many cities and their operation for a number of years, the levels of usage of PGI remain much lower than expected. To guide investment and operational decisions, this study examines the existing PGI systems from the drivers' perspective. The results show that PGI is not efficiently used and often ignored by drivers because of the inaccurate or out-of-date nature of the information it is displaying. Habitual behaviour also played an important role in the choices of a car park. However, the results of the research also show that there is a desire for more accurate, dynamic and personalised parking information through different means at pre-trip stage and en-route stage. The results of this survey should provide some guidance in the design of future PGI systems.

Monovision-based vehicle detection, distance and relative speed measurement in urban traffic

Abstract: This study presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey-scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

Detection of potholes in autonomous vehicle

Abstract: Pothole avoidance may be considered similar to other obstacle avoidance, except that the potholes are depressions rather than extrusions from a surface. This study discusses a solution for detection of potholes in the path of an autonomous vehicle operating in an unstructured environment. Here, a vision approach is used since the simulated potholes are significantly different from the background surface. Furthermore, using this approach, pothole can only be detected in case of uniform lighting conditions. The solution to the problem is developed in a systematic manner. Initially, a specific camera and frame grabber are chosen, then camera is mounted on top of the autonomous vehicle and the images will be acquired. Then, a software solution is designed using MATLAB. The method is tested under real-time conditions and results demonstrate its reasonable efficiency.

Global sensitivity analysis techniques to simplify the calibration of traffic simulation models. Methodology and application to the IDM car-following model

Abstract: As models are simplifications of reality, the management of the uncertainty arising along the whole modelling process is a crucial and delicate operation, which primarily affects the credibility of model results. In the field of traffic simulation to tackle this issue, it is common practice to include the model uncertainty alongside the uncertainty in the parametric inputs. However, reducing the uncertainty in the modelling process through the indirect estimation of the model parameters is far from being simple. In this picture, a key role can be played by model sensitivity analysis. In the present work, in particular, the role of sensitivity analysis in the management of modelling uncertainties is firstly illustrated. Then, one of the most advanced techniques to perform sensitivity analysis is explained and applied to identify, in the specific context of application, which of the input factors of two car-following models can be fixed without appreciably affecting a specific output of interest. Results confirmed the relevance of sensitivity analysis in driving analysts' activities for models' comprehension, calibration and validation, namely, for their appropriate use.

Machine-learning methodology for energy efficient routing

Abstract: Eco-driving assistance systems encourage economical driving behaviour and support the driver in optimising his/her driving style to achieve fuel economy and consequently, emission reductions. Energy efficiency is also one of the most pertinent issues related to the autonomy of fully electric vehicles. This study introduces a novel methodology for energy efficient routing, based on the realisation of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, performed mainly by means of machine-learning functionality. This proposed innovative methodology, the functional architecture implementing it, as well as demonstrative experimental results are presented in this study.

Scene-based pedestrian safety performance model in mixed traffic situation

Abstract: Compared to vehicle-only collisions, traffic collisions with pedestrians are studied less because of insufficient data. However, with the development of image processing technology, a growing number of road user behavioural analyses have been conducted using video data. This study tries to extract road users' movements from video data in order to analyse the conflict between pedestrian and vehicle and to evaluate pedestrian safety performance during conflicts. The time difference to collision (TDTC) parameter is used to fit the safety analysis on pedestrian-involved conflicts. Scene-based analysis, which evaluates the safety performance of traffic intersections and segments where several pedestrian-vehicle conflicts may happen together, is conducted using 91 groups of scene data. The parameters most related to pedestrian safety are located using a sensitivity test, a quantitative definition of pedestrian-vehicle conflict is then defined, and a scene-based pedestrian safety performance evaluation model is built. The model can correctly detect nearly 94.4% of possibly dangerous traffic scenes. Other kinds of mixed traffic scenes can also be studied based on this research.

A Gaussian mixture model and support vector machine approach to vehicle type and colour classification

Abstract: The authors describe their approach to segmenting moving road vehicles from the colour video data supplied by a stationary roadside closed-circuit television (CCTV) camera and classifying those vehicles in terms of type (car, van and heavy goods vehicle) and dominant colour. For the segmentation, the authors use a recursively updated Gaussian mixture model approach, with a multi-dimensional smoothing transform. The authors show that this transform improves the segmentation performance, particularly in adverse imaging conditions, such as when there is camera vibration. The authors then present a comprehensive comparative evaluation of shadow detection approaches, which is an essential component of background subtraction in outdoor scenes. For vehicle classification, a practical and systematic approach using a kernelised support vector machine is developed. The good recognition rates achieved in the authors' experiments indicate that their approach is well suited for pragmatic vehicle classification applications.

Cooperative vehicle collision warning system using the vector-based approach with dedicated short range communication data transmission

Abstract: There are three challenges that have to be overcome in order to enhance the accuracy of the cooperative collision warning (CCW) system. First, a vehicle does not always keep the same velocity. Second, the curve road condition should be considered. Third, position interruption caused by Global Positioning System positioning delay has to be tackled. In this study, the authors proposed a vector-based CCW system named VCCW to deal with these three challenges. The simulation results show that the proposed VCCW system has high accuracy's warning rates in both intersection and curve situations. The authors have also implemented the VCCW and tested the system on real roads. Through the VCCW system experiment on real roads, it shows that the VCCW system can alert drivers before collision.

Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

Abstract: In the past decades, different forms of car-following behaviour model have been intensively studied, proposed and implemented. These models are increasingly used by transportation experts to utilise for appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, an improved adaptive neuro fuzzy inference system (ANFIS) model is proposed to simulate and predict the car-following behaviour based on the reaction delay of the driver–vehicle unit. An idea is proposed to calculate the reaction delay. In this model, the reaction delay is used as an input and other inputs–outputs of the model are chosen with respect to this parameter. Using the real-world data, the performance of the model is evaluated and compared with the responses of other existing ANFIS car-following models. The simulation results show that the proposed model has a very close compatibility with the real-world data and reflects the situation of the traffic flow in a more realistic way. Also, the comparison shows that the error of the proposed model is smaller than that in the other models.

Getting drivers to do the right thing: a review of the potential for safely reducing energy consumption through design

Abstract: Transport, particularly private vehicle use, contributes a disproportionately large amount to the degradation of the environment. Although advancements in energy production and vehicular technologies are critical for abatement, behaviour change will also have to be seen, hence the requirement for the application of Ergonomics. This review article aims to bring together various strands of research, including the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity and novelty. Finally, it is argued that Ecological Interface Design, in presenting the constraints of the system to the driver, is well suited to deal with the novelty of the low-carbon vehicle, supports the development of accurate mental-models of the system, and can be used for the design of in-vehicle interfaces that encourage energy-conserving driving behaviours while minimising distraction and workload, thus ensuring safety.

Fuel consumption and gas emissions of an automatic transmission vehicle following simple eco-driving instructions on urban roads

Abstract: Following eco-driving instructions can reduce fuel consumption between 5 to 20% on urban roads with manual cars. The majority of Australian cars have an automatic transmission gear-box. It is therefore of interest to verify whether current eco-driving instructions are e cient for such vehicles. In this pilot study, participants (N=13) drove an instrumented vehicle (Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants was compared before and after they received simple eco-driving instructions. Participants drove the same vehicle on the same urban route under similar tra c conditions. We found that participants drove at similar speeds during their baseline and eco-friendly drives, and reduced the level of their accelerations and decelerations during eco-driving. Fuel consumption decreased for the complete drive by 7%, but not on the motorway and inclined sections of the study. Gas emissions were estimated with the VT-micro model, and emissions of the studied pollutants (CO2, CO, NOX and HC) were reduced, but no di erence was observed for CO2 on the motorway and inclined sections. The di erence for the complete lap is 3% for CO2. We have found evidence showing that simple eco-driving instructions are e cient in the case of automatic transmission in an urban environment, but towards the lowest values of the spectrum of fuel consumption reduction from the di erent eco-driving studies.

Microscopic cooperative traffic flow: calibration and simulation based on a next generation simulation dataset

Abstract: The deployment of new emerging technologies, such as cooperative systems, allows the traffic community to foresee relevant improvements in terms of traffic safety and efficiency. Autonomous vehicles are able to share information about the local traffic state in real time, which could result in a better reaction to the mechanism of traffic jam formation. An upstream single-hop radio broadcast network can improve the perception of each cooperative driver within a specific radio range and hence the traffic stability. The impact of vehicle to vehicle cooperation on the onset of traffic congestion is investigated analytically and through simulation. A next generation simulation field dataset is used to calibrate the full velocity difference car-following model, and the MOBIL lane-changing model is implemented. The robustness of the calibration as well as the heterogeneity of the drivers is discussed. Assuming that congestion can be triggered either by the heterogeneity of drivers' behaviours or abnormal lane-changing behaviours, the calibrated car-following model is used to assess the impact of a microscopic cooperative law on egoistic lane-changing behaviours. The cooperative law can help reduce and delay traffic congestion and can have a positive effect on safety indicators.

Empirical analysis of the causes of stop-and-go waves at sags

Abstract: Stop-and-go waves are spatially-confined regions of low traffic speed that propagate upstream at a constant velocity. The occurrence of stop-and-go waves on freeways has negative impacts on both travel time and traffic safety. Sags are freeway sections along which gradient changes significantly from downwards to upwards. Stop-and-go waves often emerge on the uphill section of sags, both in uncongested and congested traffic conditions. According to previous studies, the formation of stop-and-go waves at sags can be caused by local changes in car-following behaviour as well as disruptive lane changes. However, it is not clear which of those two causes is more frequent. This paper aims to identify the primary factor triggering stop-and-go waves at sags. To this end, the authors analyse vehicle trajectories collected by means of video cameras on a three-lane sag of the Tomei Expressway (Japan), identifying the causes of formation and growth of stop-and-go waves on the study site. The results show that the primary factor triggering stop-and-go waves is related to car-following behaviour. This finding shows the relevance of developing systems to assist drivers in performing the acceleration task at sags.

Design, implementation and optimisation of an energy harvesting system for vehicular ad hoc networks' road side units

Abstract: Vehicular ad hoc network (VANET) research has spanned a wide variety of topics in recent years. This study deals with a subject rarely discussed in the literatures, the design procedure of a road side unit (RSU) armed with solar energy-harvesting circuit and its power management module. Embedded UBICOM IP2022 platform was adopted to implement the intended RSU. A complete design steps of the electronic circuit were described and the necessary values of the system component, that is, solar cell panels, battery cells and the DC-DC converter was tuned to suite the design goals. In order to decrease the power consumption of the suggested RSU and to extend the lifetime of the batteries, a power management module based on an artificial neural network and green scheduler was suggested. This scheduler is located in the control centre and composed of three algorithms in order of execution: the prediction, ON/OFF and evaluation algorithms. The adoption of the green scheduler reduces the power consumption of the nodes, which extends the battery life and decreases the number of the required battery cells.

Driving behaviour-based event data recorder

Abstract: A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.

Dynamic and stochastic routing for multimodal transportation systems

Abstract: The authors present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently. This travel time information can either be static or dynamic, or either deterministic or stochastic. Next, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like the modelling of the stochastic distributions, comparing these distributions, and so on. A tradeoff had to be made between the performance of the system and the accuracy of the results. Experiments have shown that the proposed system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster), more reliable routes can be calculated. Moreover, it is shown that routing in the multimodal network may have its advantages over routing in a unimodal network, especially during rush hours.

Optimised speed profile design of a vehicle platoon considering road inclinations

Abstract: This study focuses on the design of a platoon control system which takes into consideration safe travel by using the string stability theorem and the knowledge of the inclinations of the road along the route of the platoon. By choosing the speed of the platoon fitting in with the inclinations of the road the number of unnecessary accelerations and brakings can be reduced, thus so can the operations of the actuators of the vehicles, that is, the driveline and the brake system. Although the longitudinal dynamics of the vehicles is formulated in a linear control-oriented model, the non-linear performance of the road inclinations and safety requirements based on the string stability are taken into consideration. The design of the platoon control is based on the robust H∞ control theory.

Real-time estimation of travel speed using urban traffic information system and filtering algorithm

Abstract: Travel speed is an important parameter for measuring road traffic. urban traffic information system (UTIS) was developed as a mobile detector for measuring link travel speeds in South Korea. However, UTIS incur errors, such as those caused by irregular vehicle trajectories and communication delays. This study describes an algorithm developed for estimating reliable and accurate average roadway link travel speeds using UTIS data. The algorithm estimates link travel times using a robust data-filtering procedure to identify valid observations within a sampling interval using a varying data validity window. The size of the data validity window varies as a function of the standard deviation of observations in previous intervals. A field test showed that the variance of the percent errors of link travel times was reduced when measured using the new model. Therefore it can be concluded that the proposed model significantly improves travel speed measuring accuracy.

Modelling and simulating worm propagation in static and dynamic traffic

Abstract: Vehicular ad hoc networks (VANETs) have no fixed infrastructure and instead relies on the vehicles themselves to provide network functionality. An attack scenario with potentially catastrophic consequences is the outbreak of mobile worm epidemic in these networks. This paper analyses the snapshot spreading results under an urban scenario with equilibrium traffic through modelling the mobility pattern, the communication channel, the medium access control (MAC) mechanism and the worm propagation process. The extensive Monte Carlo simulations uncovered the effects of the transmission range (from a typical minimum to a maximum), the minimum velocity and the maximum velocity (from the free flow to the congested traffic), the vehicle density (from a sparse topology to a dense spatial relation) and the MAC mechanism (from presence to absence) on epidemic spreading of such worms in VANETs. Furthermore, the authors simulate the wireless worm propagation in dynamic traffic with the same scenario as the static traffic by using a network simulation tool. The authors discuss the correlation between snapshot results and evolutive outcome, also analyse the reasons resulting in the local differences and finally uncover the interrelations between the affected rate and network parameters. The results are expected to help engineers design intelligent and automatic detection prevention strategies for VANETs.

Elderly driver retraining using automatic evaluation system of safe driving skill

Abstract: In Japan, although the rapid aging of the population has caused serious traffic problems, only a few studies have investigated the behaviour of elderly drivers in real traffic conditions. The authors have been developing a system to automatically evaluate safe-driving skill through small wireless wearable sensors that directly measure the driver's behaviour. The authors aim is to promote safe driving by providing a personalised training program according to the individual's own shortcomings in driving behaviour. By employing the sensors together with GPS and driving instructors' knowledge, our system can automatically identify shortcomings in driving skill with an accuracy of over 80%. In February 2010, the Kyoto Prefecture Public Safety Commission, in Japan, certified our system as the first and only support tool for its `mandatory retraining course for elderly drivers' that all elderly drivers, aged over 70 years, are required to take when renewing their driver's license. In this study, the authors discuss the effectiveness of our system and investigate elderly drivers' behaviour through a large-scale demonstration experiment, involving 749 elderly drivers, in the mandatory driver-retraining course on public roads. The authors results reveal that although elderly drivers are able to maintain a safe vehicle speed, their tendency to not scan around their vehicle to ensure safety makes their driving risky.

Infrared-based vehicular positioning with the automatic radiation-strength control

Abstract: Infrared dedicated short-range communication (IR-DSRC) has been used in single-lane vehicle-to-roadside applications, and is challenged for its application in multi-lane-free-flow vehicle-to-vehicle (V2V) conditions. However, based on IR positioning technology, the IR-DSRC is expected to be useful and valuable under V2V conditions for several intelligent-transportation-system applications. In the previous studies, a three-dimensional IR positioning system was developed to position a preceding vehicle. However, the positioning performance degrades as the longitudinal or the lateral distance between vehicles increases because the resolution of the received signal is deteriorated for weak signals. In this study, onboard units (OBUs) with wide radiation-pattern designs and automatic radiation-strength control are realised to provide stable signal strength for positioning, and the non-ideality of the IR positioning system is compensated by the calibrating procedures as well. A two-vehicle trial is performed, in which the positioning OBU is installed on the most front of the following vehicle to reduce the scattering effect of the vehicle body. The experimental result demonstrates that the positioning performance is greatly improved after aforementioned improvements.

Conditional inference tree-based analysis of hazardous traffic conditions for rear-end and sideswipe collisions with implications for control strategies on freeways

Abstract: Identifying hazardous traffic conditions related to traffic collisions is important to the development of real-time traffic control measures for preventing collision occurrences. The primary objective of this study is to explore the hazardous traffic situations on freeways for rear-end and sideswipe collisions to assist the development of control strategies for mitigating collision risks using the conditional inference tree method. Based on the 3-year crash data and traffic data from a freeway corridor on the Interstate 880 in California, the conditional inference tree was developed and validated for each collision type separately. Results showed that the hazardous traffic conditions were different between different types of collisions. The occurrence of rear-end collision was mainly related to the magnitude of lengthwise traffic variation between upstream and downstream locations. The occurrence of sideswipe collision was related to the type of freeway segment as well as the crosswise traffic variation between adjacent lanes. The control strategies for the mitigation of collision potentials were discussed according to the appearances of the conditional inference trees developed in this study.