In this study, the authors present a simple method to estimate intrinsic and extrinsic parameters of the camera for roundabout traffic scenes. Unlike many previous works on camera calibration for roadway scenes by using parallel lines, the proposed work addresses camera calibration for roundabout scenes by using a circle. The proposed method can estimate focal length, pan angle, tilt angle and camera height by matching the ellipse equation extracted from an image with the perspective-transformed equation of the corresponding real-world circle. This method requires only one image for camera calibration. The method is validated with real-world roundabout traffic scenes and the calibration results are reasonably accurate compared with ground truth measurements.

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.

In this study, a multi-stakeholder/multi-actor multi-criteria analysis (MAMCA) is applied to obtain a selection and preliminary ranking for a number of alternative ways to design innovative ITS-based tools that have the potential to improve road safety by creating a more forgiving road (FOR) and a more self-explaining road (SER) environment. The aim of this MCA is to allow various stakeholders with an interest in improving road safety (in particular public policymakers, but also users and manufacturers), to assess alternative designs for future FOR and SER environments. For each of these stakeholders a separate MCA is carried out taking into account their specific objectives. By analysing the differences in preferences among stakeholders and using the highest expected ‘value-added’ from the community of stakeholders in its entirety on various FOR and SER environments, it has been possible to uncover information on the chances of successful implementation.

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.

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.

In order to control urban traffic with model-based control methods, a proper traffic model is very important. This traffic control model needs to have enough descriptive power to reproduce relevant traffic phenomena, and it also has to be fast enough to be used in practice. Consequently, macroscopic urban traffic flow models are usually applied as control models. In this study, a macroscopic spatiotemporally discrete urban traffic model with a variable sampling time interval is proposed for model-based control strategies. By selecting proper sampling time intervals and sampling space distances, it allows us to balance modelling accuracy and computational complexity of the spatiotemporally discrete model. In addition, an urban traffic Courant–Friedrichs–Lewy (CFL) condition is deduced for spatiotemporally discrete urban traffic models, which is a sufficient condition to guarantee the discrete model to bear enough descriptive modelling power to reproduce necessary traffic phenomena. The model is analysed and evaluated based on the model requirements for control purposes. The simulation results are compared for the situations where the CFL condition is violated and not violated.

To improve the quality of vehicular following behaviour in safety and efficiency, a new method, which combines the velocity difference control with the dynamic tracking of safe following distance closely, is presented to realise the dynamic optimisation of the following vehicle's behaviour and the spatial interval between the preceding and following vehicles. The corresponding mathematical model is given in this study, as well as control system design and the control algorithms for the following vehicle to adjust its own behaviour scientifically. Different from other vehicle following models, the fitting function is used to calculate dynamic safe following distance, which should be kept by the following vehicle from the preceding vehicle. The safe following distance can be obtained in real time by the following vehicle at any velocity from 0 to 500 km/h for the evaluation and the scientific adjustment of its own behaviour. The simulation verifies the feasibility and validity of this method, which can ensure the safe, efficient and smooth (comfort) operation of the following vehicle in its following process.

In this study, the authors propose an effective real-time approach for intersection detection and recognition during autonomous driving in an unknown urban environment. The authors approach use point cloud data acquired by a three-dimensional laser scanner mounted on the vehicle. Intersection detection and recognition are formulated as a classification problem whereby roads are classified as segments or intersections and intersections are subclassified as T-shaped or +-shaped. They first construct a novel model called a virtual cylindrical scanner for efficient feature-level representation of the point cloud data. Then they use support vector machine classifiers to resolve the classification problem according to the features extracted. A series of experiments on real-world data sets and in a simulation environment demonstrate the effectiveness and robustness of the authors approach, even in highly dynamic urban environment. They also performed simulation experiments to investigate effects of several critical factors on their proposed approach, such as other vehicles on the road and the advance detection distance.

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.

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 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.

Anticipatory traveller information system based on variable message sign (VMS) aims to relieve recurrent and non-recurrent congestion by using predicted traffic conditions to design control strategies consistent with drivers’ route choice behaviour to guide drivers through the freeway-arterial networks and improve system performance. To provide anticipatory traveller information consistent with drivers’ en-route diversion behaviour and controller's objectives effectively, this study examines (i) automated calibration of the parameters of a time-variant drivers’ en-route diversion model using aggregate-level data, and (ii) an active route guidance scheme, which projects the network states into the near future and determines spatiotemporal coordinated anticipatory traveller information. The proposed system is tested on real world networks. The implementation results demonstrate that the anticipatory traveller information provided by the proposed model is more consistent, and therefore easier to be accepted by drivers. The findings also reveal that the coordinated VMS-based anticipatory traveller information effectively provides guidance to drivers because it accounts for the spatial and temporal changes in traffic conditions, and encourages better use of roadway capacity, thereby resulting in better system performance over the same period of operations.

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.

Traffic control and management needs accurate estimation and prediction of traffic variables such as flow, speed, volume, travel time etc. Linear stochastic time series methods are powerful analytical tools. The capability of state space reconstruction makes them popular in traffic prediction and estimation. In this study, to overcome the linearity assumption of these methods, a non-linear kernel-based stochastic time series method with state space reconstruction capability is proposed. To minimise the prediction error of the method, adaptive time variant transformation from primary space to reproducing kernel Hilbert space is proposed by employing extended Kalman filter. Owing to high costs of traffic detectors, not all the metropolitan areas are equipped with these sensors; therefore in this study, an extended Kalman observer based on the new dynamic-adaptive-non-linear predictor is designed and applied for traffic flow estimation and prediction in the areas that suffer from lack of detectors. Practical data simulations and evaluations justify the high strength and accuracy of the proposed method in prediction of traffic speed with incomplete data sources.