Characteristics of wrong-way incidents and crashes that occurred on the entire motorway network in Japan are analysed in this study with an emphasis on wrong-way crashes. Nearly 40% of vehicles in wrong-way crashes took U-turns on the main carriageway, followed by 20% entering the wrong way at interchanges after passing the tollgate, 18% before passing the tollgate and 12% at rest areas. Wrong entries and suspected dementia were the two main contributing factors for wrong-way crashes, each accounting for nearly 30% of the total number of wrong-way crashes, followed by each 8–10% for confusion with ordinary road, taking U-turns on the main carriageway and driving under the influence of alcohol. Most wrong-way crashes because of wrong entries were caused by older drivers over the age of 60 (61%) and young drivers (22%) and most of those because of confusion with ordinary road were also caused by older drivers (86%). All the wrong-way crashes caused by suspected dementia were by older drivers over the age of 65 and occurred between 4–10 p.m. Finally some applications of recent ITS technologies to prevent wrong-way driving that have been implemented recently on motorways in Japan are briefly introduced.

Research on the acceptability of assistive systems for improving the safety of powered two-wheelers (PTWs) is a pressing issue. The use of safety-enhancing assistive systems for motorised vehicles, including advanced driver assistance systems and in-vehicle information systems is widespread in many countries. Yet, there is only a limited number of equivalent intelligent transport systems (ITS) for PTWs, namely advanced rider assistance systems and on-bike information systems. This study describes the methodological development of a specific tool for assessing motorcyclists’ acceptability of ITS, as part of the motorcyclists’ profiling questionnaire (MOPROQ). There were three stages of development. First, a literature review was undertaken to assess the current state of the art regarding ITS for PTWs and to determine the most relevant facets of acceptability that should be measured. Second, a series of focus group interviews were conducted to explore riders’ attitudes towards ITS. Finally, the focus group results were used to develop a large-scale survey (MOPROQ), which was administered to an initial sample of over 6000 riders internationally. The designed tool can be used as a basis for the determination of rider acceptability of ITS systems in the future.

Incidents can significantly impact freeway operations and deteriorate mobility and safety. This study quantified their safety impacts through inspecting queue properties. Specifically, the end-of-queue (EOQ), where severe rear-end collisions commonly occur, is employed for safety assessment based on vehicles’ trajectories. Since detector data is typically available, this study applied such data for EOQ identification, as opposed to vehicle trajectories that are difficult to collect and process. Three measures related to queue duration, impact area and vehicle number exposed to the EOQ are presented as surrogates. To understand the applicability of these measures, a systematic set of incident scenarios is replicated with VISSIM. Quantitative results are used to estimate regression models, and significant variables are identified. The proposed methods can be used to evaluate safety impact of traffic incident management programs such as freeway service patrol, as well as to determine optimal plans for prearranged incidents such as pothole repair.

The growing interest in reducing fuel consumption and gas emissions provides an incentive for the automotive industry to innovate in the field of hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). The two embedded power sources in these vehicles require an intelligent controller in order to make the best decision on the power distribution. Actually these controllers, often called energy management systems, are very important and greatly influence the achievable fuel economy. Compared with an HEV, a PHEV allows battery discharge over a complete trip. As a consequence the optimal control of a PHEV implies a stronger dependence on the total driving cycle. Many authors have studied the possibility of fuzzy-based systems for both HEV and PHEV as they have proved to be robust, reliable and simple. However, classical fuzzy rule-based strategies demonstrate a lack of optimality because their design is focused on the actual vehicle state rather than the driving conditions. This study proposes a blended control strategy based on fuzzy logic for a PHEV. The proposed controller is fed with driving condition information in order to increase the controller effectiveness in every situation. The efficiency of the proposed controller is demonstrated through simulations.

Here, the authors propose the concept of a local electricity-sharing system as a complement or alternative to a feed-in tariff to achieve CO₂-neutral transportation in cities. In the authors’ proposed system, electricity generated from widely introduced solar photovoltaic panels (PVs) is stored in the cars ‘not in use’ in a city. In Japan, almost half of the cars in the central Tokyo metropolitan area are used only on weekends and thus are kept parked during weekdays. These cars represent a huge new potential storage depot if they were replaced by electric vehicles (EVs), that is, they could be used as storage batteries in a vehicle-to-grid system. Using an agent-based transportation simulator (MATSim), the authors modelled the effects of a large-scale introduction of EVs and PVs on the hourly regional power demand–supply for Yokohama city, Japan. The results of our simulation suggest that CO₂-neutral EV trips can be achieved only during periods with current average solar irradiance.

The implementation of anticipating driving styles in adaptive cruise control systems promises to considerably reduce fuel consumption of vehicles. As drivers have to accept the optimised driving styles of such systems, which implement longitudinally automated driving, the optimisation results should not deviate strongly from the average driving behaviour. This work presents an approach to the optimisation of the vehicle's longitudinal dynamics, which is based on a predicted average driving profile. The proposed approach ensures that the optimisation results meet the expectations of drivers by directly accounting for driver's preferences on weighting up travel time against fuel consumption relative to the average driving profile. Based on human decision finding, rational and intuitive planning decisions are modelled in a cost function and represent optimisation constraints. The approach generally includes information from vehicle-to-vehicle and vehicle-to-infrastructure communication (V2X), which is an extension to the state-of-the-art. This study describes the optimisation approach and presents a method to determine suitable optimisation parameters in order to consider driver's preferences. The optimisation approach is applied in a simulated test drive and improvements in fuel economy are analysed. Finally, the authors sketch a reference system architecture to prove the feasibility of the presented approach.

In this study, the authors analyse the exponential growth of advanced driver assistance systems based on video processing in the past decade. Specifically, they focus on how research and innovative ideas can finally reach the market as cost-effective solutions. They explore well-known computer vision methods for services like lane departure warning systems, collision avoidance systems and point out potential future trends according to a review of the state-of-the-art. Along this study, the authors’ own contributions are described as examples of such systems from the perspective of real-time by design, pursuing a trade-off between the accuracy and reliability of the designed algorithms and the restrictive computational, economical and design requisites of embedded platforms.

The signalling system is one of the most costly systems in the railway infrastructure. The cost of the trackside controllers is particularly high because high-performance failsafe hardware and software are needed to supervise and control the many pieces of the field equipment. A signalling system is described that has a lower projected cost because of the application of the traditional token block mechanism to the virtual blocks dividing each controlled section. Its basic mechanism is the sharing by the wireless communication of the information about the exclusive rights with the virtual blocks, the status of the switches and the instructions for controlling the switches in the controlled section among the onboard controllers and the switch controllers, respectively. A telegram for each controlled section is circulated in turn among these controllers for the sharing. This mechanism provides an automatic train protection and an interlocking function without using the trackside controllers. Its application to a very high-density line might reduce the train operation density because of the telegram circulation time, but the reduction would be no greater than 20% on the highest density line if the current radio technology was used. A cost evaluation based on a 20-km double-track line showed that this system is about three times more cost effective than the communication-based train control system.

The use of night vision systems in vehicles is becoming increasingly common, not just in luxury cars but also in the more cost sensitive sectors. Numerous approaches using infrared sensors have been proposed in the literature to detect and classify pedestrians in low visibility situations. However, the performance of these systems is limited by the capability of the classifier. This paper presents a novel method of classifying pedestrians in far-infrared automotive imagery. Regions of interest are segmented from the infrared frame using seeded region growing. A novel method of filtering the region growing results based on the location and size of the bounding box within the frame is described. This results in a smaller number of regions of interest for classification, leading to a reduced false positive rate. Histograms of oriented gradient features and local binary pattern features are extracted from the regions of interest and concatenated to form a feature for classification. Pedestrians are tracked with a Kalman filter to increase detection rates and system robustness. Detection rates of 98%, and false positive rates of 1% have been achieved on a database of 2000 images and streams of video; this is a 3% improvement on previously reported detection rates.

The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie–Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

Studies concerning collision avoidance show that most drivers tend to brake, even if evasive manoeuvres were better. Automatic steering for collision avoidance would help here. Studies in the EU project interactIVe observed that drivers show a tendency to hold the steering wheel and disturb the automatic steering when kept in the control loop. A strategy of driver decoupling, for example, by means of steer-by-wire systems could improve the automatic steering performance. However, the major challenge of using steer-by-wire systems is to enable the driver to compensate false system activation, for example, evasion into oncoming traffic. A time-dependent strategy of driver decoupling using steer-by-wire combined with override recognition by counter steering above a certain threshold was implemented in a research vehicle. The interaction strategy was tested with 45 participants on a test track in two different scenarios; a collision situation with justified evasion and a false alert scenario with unjustified system activation. In a between-subject design the decoupling strategy (using steer-by-wire) was compared against automatic steering with fully coupled driver and force feedback on the steering wheel and against Manual Driving without automatic steering. When the driver was temporarily decoupled, the obstacle avoidance performance was better but the driver was less able to counteract a false avoidance manoeuvre. The analysis of driver behaviour revealed options to improve the interaction strategy.

In this study, a collision avoidance system is presented, based on the information provided by a laser-scanner sensor, in which two actions could be taken in case of danger. Firstly, the system tries to stop the vehicle in order to avoid the accident. If a reduction in speed is not sufficiently effective, the control system takes control of the steering and deviates the vehicle's trajectory in order to escape from the hazardous situation. The control system evaluates the situation and decides the most appropriate action in each case considering free areas on the surroundings using the information of a detailed digital map. This system has been implemented in a vehicle and has been tested with pedestrians and vehicles circulating along the private test track with satisfactory results.