Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Managing multiple markets: big firms and PFI contracts

The Private Finance Initiative (PFI) is frequently portrayed as a vehicle for change for the UK construction sector. Significant change in the working practices of construction companies is predicted as new business models based on whole-life value creation emerge. This paper shifts the focus of discussion from projected ideals and possible developments to the current situation. More specifically, it focuses on the challenges that large firms participating in both PFI and traditional markets face. The analysis focuses on the relations between business units and on day-to-day challenges to greater long-term commitment, through life-service provision and increased integration between construction and service provision. The paper offers insights into the effects of PFI on construction practice and their implications for theorizing on organizational and strategic change. It suggests abandoning a simplistic model of the centralized, homogenous firm and instead capturing the dynamics of decentralized, large firms working in multiple markets on a variety of projects. This would assist in the provision of more realistic and fruitful models of how to realize the PFI vision.

Multi-vehicle planning using RRT-connect

The problem of planning multiple vehicles deals with the design of an effective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it suboptimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Diagnostic de securite d'une entreprise bresilienne a partir de l'analyse des accidents du travail par la methode de l'arbre des causes

We present here the results of a study of 21 work-related accidents that occurred in a Brazilian manufacturing company. The aim was to assess the safety level of the company to improve its work accident prevention policy. In the last 6 months of 1992 and 1993, all accidents resulting in 15 days' absence from work, reported for social security purposes, were analyzed using the INRS causal tree method (ADC) and a questionnaire completed on site. Potential risk factors for accidents were identified based on the specific factors highlighted by the ADC. More universal trees were also compiled for the safety assessment. Three hundred and thirty specific accident factors were recorded (man of 15.71 per accident). This is consistent with there being multiple causes of accidents rather than the assertion of Brazilian business safety departments that accidents are due to 'dangerous' or 'unsafe' behavior. Introducing the idea of culpability into accidents prevents the implementation of an appropriate information feedback process, essential for effective prevention. However, the large number of accidents related to 'material' (78%) and 'environment' (70%) indicates that working conditions are poor. This shows that the technical risks, mostly due to unsafe machinery and equipment are not being dealt with. Seventy-five potential accident factors were identified. Of these, 35% were 'organizational', a high proportion for the company studied. Improvisation occurs at all levels, particularly at the organizational level. This is, thus a major determinant for entire series of, if not most, accident situations. The poor condition of equipment also plays a major role in accidents. The effects of poor equipment on safety exacerbate the organizational shortcomings. The company's safety intervention policy should improve the management of human resources (rules designating particular workers for particular workstations; instructions for the safe operation of machines and equipment; training of operators, etc.) and introduce programs to detect risks and to improve the safety of machines and equipment. We also recommend the establishment of a program to follow the results of any preventive measures adopted.

Diagnostic de sécurité d’une entreprise brésilienne à partir de l’analyse des accidents du travail par la méthode de l’arbre des causes

We present here the results of a study of 21 work-related accidents that occurred in a Brazilian manufacturing company. The aim was to assess the safety level of the company to improve its work accident prevention policy. In the last 6 months of 1992 and 1993, all accidents resulting in 15 days' absence from work, reported for social security purposes, were analyzed using the INRS causal tree method (ADC) and a questionnaire completed on site. Potential risk factors for accidents were identified based on the specific factors highlighted by the ADC. More universal trees were also compiled for the safety assessment. Three hundred and thirty specific accident factors were recorded (mean of 15.71 per accident). This is consistent with there being multiple causes of accidents rather than the assertion of Brazilian business safety departments that accidents are due to dangerous or unsafe behavior. Introducing the idea of culpability into accidents prevents the implementation of an appropriate information feedback process, essential for effective prevention. However, the large number of accidents related to material (78%) and environment (70%) indicates that working conditions are poor. This shows that the technical risks, mostly due to unsafe machinery and equipment are not being dealt with. Seventy-five potential accident factors were identified. Of these, 35% were organizational, a high proportion for the company studied. Improvisation occurs at all levels, particularly at the organizational level. This is thus a major determinant for entire series of, if not most, accident situations. The poor condition of equipment also plays a major role in accidents. The effects of poor equipment on safety exacerbate the organizational shortcomings. The company's safety intervention policy should improve the management of human resources (rules designating particular workers for particular workstations; instructions for the safe operation of machines and equipment; training of operators, etc.) and introduce programs to detect risks and to improve the safety of machines and equipment. We also recommend the establishment of a program to follow the results of any preventive measures adopted.

An investigation into the loss of the Brazilian space programme's launch vehicle VLS-1 V03

There has been a rapid increase in the complexity and integration of many safety-critical systems. In consequence, it is becoming increasingly difficult to identify the causes of incidents and accidents back through the complex interactions that lead to an adverse event. At the same time, there is a growing appreciation of the need to consider a broad range of contextual factors in the aftermath of any mishap. A number of regulators, operators and research teams have responded to these developments by proposing novel techniques to support the analysis of complex, safety-critical incidents. It is important to illustrate these different approaches by applying them to a number of common case studies. The following pages, therefore, show how STAMP and AcciMap might support the Serviço Público Federal investigation into the explosion and fire of the Brazilian launch vehicle VLS-1 VO3. © 2006 Elsevier Ltd. All rights reserved.

Mortality from road traffic accidents in Switzerland: longitudinal and spatial analyses

Road traffic accidents (RTA) are an important cause of premature death. We examined socio-demographic and geographical determinants of RTA mortality in Switzerland by linking 2000 census data to RTA mortality records 2000-2005 (ICD-10 codes V00-V99). Data from 5.5 million residents aged 18-94 years, 1744 study areas, and 1620 RTA deaths were analyzed, including 978 deaths (60.4%) in motor vehicle occupants, 254 (15.7%) in motorcyclists, 107 (6.6%) in cyclists, and 259 (16.0%) in pedestrians. Weibull survival models and Bayesian methods were used to calculate hazard ratios (HR), and standardized mortality ratios (SMR) across study areas. Adjusted HR comparing women with men ranged from 0.04 (95% CI 0.02-0.07) in motorcyclists to 0.43 (95% CI 0.32-0.56) in pedestrians. There was a u-shaped relationship with age in motor vehicle occupants and motorcyclists. In cyclists and pedestrians, mortality increased after age 55 years. Mortality was higher in individuals with primary education (HR 1.53; 95% CI 1.29-1.81), and higher in single (HR 1.24; 95% CI 1.05-1.46), widowed (HR 1.31; 95% CI 1.05-1.65) and divorced individuals (HR 1.62; 95% CI 1.33-1.97), compared to persons with tertiary education or married persons. The association with education was particularly strong for pedestrians (HR 1.87; 95% CI 1.20-2.91). RTA mortality increased with decreasing population density of study areas for motor vehicle occupants (test for trend p<0.0001) and motorcyclists (p=0.0021) but not for cyclists (p=0.39) or pedestrians (p=0.29). SMR standardized for socio-demographic and geographical variables ranged from 82 to 190. Prevention efforts should aim to reduce inequities across socio-demographic and educational groups, and across geographical areas, with interventions targeted at high-risk groups and areas, and different traffic users, including pedestrians.

Injury severity and mortality of adult zebra crosswalk and non-zebra crosswalk road crossing accidents: a cross-sectional analysis

PRINCIPALS Over a million people worldwide die each year from road traffic injuries and more than 10 million sustain permanent disabilities. Many of these victims are pedestrians. The present retrospective study analyzes the severity and mortality of injuries suffered by adult pedestrians, depending on whether they used a zebra crosswalk. METHODS Our retrospective data analysis covered adult patients admitted to our emergency department (ED) between 1 January 2000 and 31 December 2012 after being hit by a vehicle while crossing the road as a pedestrian. Patients were identified by using a string term. Medical, police and ambulance records were reviewed for data extraction. RESULTS A total of 347 patients were eligible for study inclusion. Two hundred and three (203; 58.5%) patients were on a zebra crosswalk and 144 (41.5%) were not. The mean ISS (injury Severity Score) was 12.1 (SD 14.7, range 1-75). The vehicles were faster in non-zebra crosswalk accidents (47.7 km/n, versus 41.4 km/h, p<0.027). The mean ISS score was higher in patients with non-zebra crosswalk accidents; 14.4 (SD 16.5, range 1-75) versus 10.5 (SD13.14, range 1-75) (p<0.019). Zebra crosswalk accidents were associated with less risk of severe injury (OR 0.61, 95% CI 0.38-0.98, p<0.042). Accidents involving a truck were associated with increased risk of severe injury (OR 3.53, 95%CI 1.21-10.26, p<0.02). CONCLUSION Accidents on zebra crosswalks are more common than those not on zebra crosswalks. The injury severity of non-zebra crosswalk accidents is significantly higher than in patients with zebra crosswalk accidents. Accidents involving large vehicles are associated with increased risk of severe injury. Further prospective studies are needed, with detailed assessment of motor vehicle types and speed.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

The interurban DRAG-Spain model: the main factors of influence on road accidents in Spain

This paper presents the results of applying DRAG methodology to the identification of the main factors of influence on the number of injury and fatal accidents occurring on Spain’s interurban network. Nineteen independent variables have been included in the model grouped together under ten categories: exposure, infrastructure, weather, drivers, economic variables, vehicle stock, surveillance, speed and legislative measures. Highly interesting conclusions can be reached from the results on the basis of the different effects of a single variable on each of the accident types according to severity. The greatest influence revealed by the results is exposure, which together with inexperienced drivers, speed and an ageing vehicle stock, have a negative effect, while the increased surveillance on roads, the improvement in the technological features of vehicles and the proportion of high capacity networks have a positive effect, since the results obtained show a significant drop in accidents.

Employment rate impacts of charging the Heavy Good Vehicle corridor (Madrid-Sevilla) across Spanish regions: a Random Utility Based Multiregional Input-Output approach

The implementation of a charging policy for heavy goods vehicles in European Union (EU) member countries has been imposed to reflect costs of construction and maintenance of infrastructure as well as externalities such as congestion, accidents and environmental impact. In this context, EU countries approved the Eurovignette directive (1999/62/EC) and its amending directive (2006 /38/EC) which established a legal framework to regulate the system of tolls. Even if that regulation seek s to increase the efficien cy of freight, it will trigger direct and indirect effects on Spain’s regional economies by increasing transport costs. This paper presents the development of a multiregional Input-Output methodology (MRIO) with elastic trade coefficients to predict in terregional trade, using transport attributes integrated in multinomial logit models. This method is highly useful to carry out an ex-ante evaluation of transport policies because it involves road freight transport cost sensitivity, and determine regional distributive and substitution economic effect s of countries like Spain, characterized by socio-demographic and economic attributes, differentiated region by region. It will thus be possible to determine cost-effective strategies, given different policy scenarios. MRIO mode l would then be used to determine the impact on the employment rate of imposing a charge in the Madrid-Sevilla corridor in Spain. This methodology is important for measuring the impact on the employment rate since it is one of the main macroeconomic indicators of Spain’s regional and national economic situation. A previous research developed (DESTINO) using a MRIO method estimated employment impacts of road pricing policy across Spanish regions considering a fuel tax charge (€/liter) in the entire shortest cost path network for freight transport. Actually, it found that the variation in employment is expected to be substantial for some regions, and negligible for others. For example, in this Spanish case study of regional employment has showed reductions between 16.1% (Rioja) and 1.4% (Madrid region). This variation range seems to be related to either the intensity of freight transport in each region or dependency of regions to transport intensive economic sect ors. In fact, regions with freight transport intensive sectors will lose more jobs while regions with a predominantly service economy undergo a fairly insignificant loss of employment. This paper is focused on evaluating a freight transport vehicle-kilometer charge (€/km) in a non-tolled motorway corridor (A-4) between Madrid-Sevilla (517 Km.). The consequences of the road pricing policy implementation show s that the employment reductions are not as high as the diminution stated in the previous research because this corridor does not affect the whole freight transport system of Spain.

HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.