Field Testing of a Cyclist Collision Avoidance System for Heavy Goods Vehicles

This research focused on preventing collisions between cyclists and heavy goods vehicles (HGVs). A collision avoidance system, designed to avoid side-to-side collisions between HGVs and cyclists, is proposed. The cyclist’s motion relative to the HGV is measured with an array of ultrasonic sensors. The detected distances from ultrasonic sensors are processed in real time to construct a smooth trajectory for the cyclist. The controller assumes constant acceleration and constant yaw rate for both the HGV and the cyclist and extrapolates the relative motion forward in time. The HGVs' brakes are engaged if a collision is predicted. A prototype system was built and fitted onto a test truck. The proposed collision avoidance system was tested in real time and proved to be effective within certain speed ranges.The authors thank the support of the Cambridge Vehicle Dynamics Consortium, whose member at the time of writing are: Anthony Best Dynamics, Camcon, Cambridge University, Denby Transport, Firestone Goodyear, Haldex, Laing O’Rourke, MIRA, SDC Trailers, SIMPACK, Tridec, Tinsley Bridge, Wincanton and Volvo Trucks. Special thanks go to Anthony Best Dynamics and Laing O'Rourke for proving essential testing equipment. Thanks also go to Dr Richard Roebuck, Dr Leon Henderson and Ms Amy Rimmer for their assistance with the testing. The authors also would like to thanks China Scholarship Council and Cambridge Trusts for their contribution to the research.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TVT.2016.253880

A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles

In 2019, more than one million crashes occurred on European roads, resulting in almost 23,000 traffic fatalities. Although heavy goods vehicles (HGVs) were only involved in 4.4% of these crashes, their proportion in crashes with fatal outcomes was almost three times larger. This over-representation of HGVs in fatal crashes calls for actions that can support the efforts to realize the vision of zero traffic fatalities in the European Union. To achieve this vision, the development and implementation of passive as well as active safety systems are necessary. To prioritise the most effective systems, safety benefit estimations need to be performed throughout the development process. The overall aim of this thesis is to provide a safety benefit assessment framework, beyond the current state of the art, which supports a timely and detailed assessment of safety systems (i.e. estimation of the change in crash and/or injury outcomes in a geographical region), in particular active safety systems for HGVs. The proposed framework is based on the systematic integration of different data sources (e.g. virtual simulations and physical tests), using Bayesian statistical methods to assess the system performance in terms of the number of lives saved and injuries avoided. The first step towards the implementation of the framework for HGVs was an analysis of three levels of crash data that identified the most common crash scenarios involving HGVs. Three scenarios were recognized: HGV striking the rear-end of another vehicle, HGV turning right in conflict with a cyclist, and HGV in conflict with a pedestrian crossing the road. Understanding road user behaviour in these critical scenarios was identified as an essential element of an accurate safety benefit assessment, but sufficiently detailed descriptions of HGV driver behaviour are currently not available. To address this research gap, a test-track experiment was conducted to collect information on HGV driver behaviour in the identified cyclist and pedestrian target scenarios. From this information, HGV driver behaviour models were created. The results show that the presence of a cyclist or pedestrian creates different speed profiles (harder braking further away from the intersection) and changes in the gaze behaviours of the HGV drivers, compared to the same situation where the vulnerable road users are not present. However, the size of the collected sample was small, which posed an obstacle to the development of meaningful driver models. To overcome this obstacle, a framework to create synthetic populations through Bayesian functional data analysis was developed and implemented. The resulting holistic safety benefit assessment framework presented in this thesis can be used not only in future studies that assess the effectiveness of safety systems for HGVs, but also during the actual development process of advanced driver assistance systems. The research results have potential implications for policies and regulations (such as new UN regulations for mandatory equipment or Euro NCAP ratings) which are based on the assessment of the real-world benefit of new safety systems and can profit from the holistic safety benefit assessment framework

Towards an Improved Safety Benefit Assessment for Heavy Trucks - Introduction of a framework for the combination of different data sources

Although heavy goods vehicles (HGVs) were only involved in 4.4% out of more than 1 million crashes that occurred on European roads in 2017, their share in crashes with fatal outcome was almost three times larger (12%). Advanced Driver Assistance Systems (ADAS) have the potential to mitigate the consequences of these crashes or avoid them altogether. In order to prioritise the most promising system, several types of safety benefit assessment are performed separately and independently of each other. These assessments miss however a combination into a common output, i.e. they are not able to provide a holistic overview but only show compartmentalised results.The first objective of this thesis is to provide a framework that can incorporate multiple data sources and combine their results into one common safety benefit output. The proposed framework within this thesis is based on Bayesian modelling and can update prior information (e.g. simulation results of a new ADAS) with new observations (e.g. test track results of the ADAS). The framework can incorporate additional information such as user acceptance and market penetration of the ADAS for an improved benefit assessment. The output of the framework can easily be incorporated as prior knowledge in new safety benefit assessments, e.g. when new data is available.The second objective is to prepare the application of the framework for the assessment of the safety benefit associated to the introduction of new ADAS for long-haul trucks. In order to specify the most critical crash scenarios for HGVs in Europe, a detailed, three-level analysis of crashes involving long-haul trucks was performed, starting on a general European level and going to in-depth crash data. The identified target scenarios are (a) rear-end crashes with the truck as the striking vehicle, (b) crashes between a right-turning truck and adjacent cyclist and (c) crashes between a truck and a pedestrian crossing in front of the truck. These three scenarios should be the basis for ADAS development and further addressed by driver behaviour modelling in the future.Future work will focus on improving simulation results by incorporating more accurate driver models, that are better able to represent truck driver behaviour, e.g. brake or steering reactions. These models will help to obtain more valid simulation results, and thereby increase the output quality of the framework

Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development

An Investigation of Higher Capacity Urban Freight Vehicles

Studies have shown that increasing the capacity of Heavy Goods Vehicles is one of the most effective ways of reducing fuel consumption per tonne-kilometre of freight moved, with consequent reductions in greenhouse and noxious emissions. Some of the disadvantages of larger vehicles are more pronounced in urban environments, including safety of other road users, and reduced manoeuvrability. This thesis discusses technologies for improving safety of vulnerable road users, and frameworks for assessing the maximum size of urban freight vehicles. An overview of the freight industry is provided in Chapter 1, with a focus on maximising capacity as a method for reducing emissions. Chapter 2 focusses on the safety of vulnerable road users, through development of a camera-based detection system for cyclists, which is essential for a predictive collision avoidance system. The proposed system is accurate to within 10 cm at distances of greater than 1 m from the vehicle, but suffers from loss of accuracy at close range, and in poor lighting conditions. The logistics of urban freight operations are analysed in Chapter 3, including a comparison between two supermarket home delivery operations, and an analysis of refuse collection schedules. A framework is proposed for selecting an optimum vehicle size for a multi-drop operation, given reductions in driving distance and time spent on other procedures. A potential capacity increase of 80% is demonstrated, requiring a 50% reduction in driving distance, and automation of certain procedures. Chapters 4 to 6 propose a novel framework for assessing the optimum size of Heavy Goods Vehicles, according to the limits of their manoeuvrability. This method is based on simulation of vehicles attempting a library of real-world manoeuvres. Simulation models are described in Chapter 4, and path planning algorithms in Chapter 5. The framework is evaluated on three case studies: a 4.25 t grocery delivery vehicle, a 44 t articulated refuse collection vehicle, and a 44 t general urban vehicle with rear axle steering. A range of potential higher capacity vehicles are proposed in Chapter 6 for those applications The impact of rear axle steering on manoeuvrability is also considered in detail in Chapter 6. It is shown that the use of rear axle steering does not always allow the use of a longer vehicle, because a rear axle steered vehicle cannot compromise between cut-in and tailswing in the way a conventional vehicle can. However, the use of rear axle steering allows reduction in both tyre wear and rear axle load limits, which permits greater vehicle fill before rear axle loads are exceeded. These results are compared, in Chapter 7, to an alternative method for modelling manoeuvrability (Performance-Based Standards). Finally, Chapter 8 presents some concluding remarks and recommendations for future work, including investigation of an improved cyclist detection system fusing cameras and ultrasonic sensors, and increased development of the manoeuvrability models to more accurately reflect real driving.This work was supported by the EPSRC, as well as the Cambridge Vehicle Dynamics Consortium, and the Centre for Sustainable Road Freigh

The development of improvements to drivers' direct and indirect vision from vehicles - phase 1

This research project concerning "The development of improvements to drivers' direct and indirect vision from vehicles" has been designed to be conducted in two phases: . Phase 1 whose aim is to scope the existing knowledge base in order to prioritise and direct activities within Phase 2; . Phase 2 whose aim is to investigate specific driver vision problems prioritised in Phase 1 and determine solutions to them. This report details the activities, findings and conclusions resulting from the Phase 1 tasks undertaken

Camera-based measurement of cyclist motion

Heavy goods vehicles are overrepresented in cyclist fatality statistics in the United Kingdom relative to their proportion of total traffic volume. In particular, the statistics highlight a problem for vehicles turning left across the path of a cyclist on their inside. In this article, we present a camera-based system to detect and track cyclists in the blind spot. The system uses boosted classifiers and geometric constraints to detect cyclist wheels, and Canny edge detection to locate the ground contact point. The locations of these points are mapped into physical coordinates using a calibration system based on the ground plane. A Kalman Filter is used to track and predict the future motion of the cyclist. Full-scale tests were conducted using a construction vehicle fitted with two cameras, and the results compared with measurements from an ultrasonic-sensor system. Errors were comparable to the ultrasonic system, with average error standard deviation of 4.3 cm when the cyclist was 1.5 m from the heavy goods vehicles, and 7.1 cm at a distance of 1 m. When results were compared to manually extracted cyclist position data, errors were less than 4 cm at separations of 1.5 and 1 m. Compared to the ultrasonic system, the camera system requires simple hardware and can easily differentiate cyclists from stationary or moving background objects such as parked cars or roadside furniture. However, the cameras suffer from reduced robustness and accuracy at close range and cannot operate in low-light conditions. C. Eddy was supported by the UK Engineering and Physical Sciences Research Council (EPSRC). C.C. de Saxe was supported by the Cambridge Commonwealth, European and International Trust, UK, and the Council for Scientific and Industrial Research (CSIR), South Africa

Understanding interactions between autonomous vehicles and other road users: A literature review

This review draws on literature relating to the interactions of vehicles with other vehicles, interactions between vehicles and infrastructure, and interactions between autonomous vehicles and cyclists and autonomous vehicles and pedestrians. The available literature relating to autonomous vehicles interactions is currently limited and hence the review has considered issues which will be relevant to autonomous vehicles from reading and evaluating a broader but still relevant literature.The project is concerned primarily with autonomous vehicles within the urban environment and hence the greatest consideration has been given to interactions on typical urban roads, with specific consideration also being given to shared space. The central questions in relation to autonomous vehicles and other road users revolve around gap acceptance, overtaking behaviour, behaviour at road narrowings, the ability to detect and avoid cyclists taking paths through a junction which conflict with the autonomous vehicle’s path, and the ability of autonomous vehicles to sense and respond to human gestures. A long list of potential research questions has been developed, many of which are not realistically answerable by the Venturer project. However, the important research questions which might potentially be answered by the current project are offered as the basis for the more detailed consideration of the conduct of the interaction trial

Analysis of good practices in Europe and Africa

According to the Global Status Report on Road Safety 2015 of WHO (WHO, 2015), “road traffic injuries claim more than 1.2 million lives each year and have a huge impact on health and development”. Based on the WHO regions, there has been a deterioration in road fatality rates in the WHO Africa region from 24.1 fatalities per 100,000 inhabitants in 2010 to 26.6 fatalities per 100,000 inhabitants in 2013. Over the same period, there was an improvement in road fatality rates in the WHO Europe region. Road trauma in Africa is expected to worsen further, with fatalities per capita projected to double over the period 2015-2030 (Small and Runji, 2014). The SaferAfrica project aims at establishing a Dialogue Platform between Africa and Europe focused on road safety and traffic management issues. It will represent a high-level body with the main objective of providing recommendations to update the African Road Safety Action Plan and the African Road Safety Charter, as well as fostering the adoption of specific initiatives, properly funded. The main objective of work package 7 (WP7) is to analyse good road safety practices realised at country, corridor and regional levels in Africa and to compare these practices with those of other countries and with international experiences. Also included in this WP7, are good practices in road safety management and in the policy-making and integration of road safety with other policy areas. WP7 includes the definition of a transferability audit, tailored to Africa conditions that can be used to assess the suitability of road safety interventions in the context of African countries. Finally, promising local projects were identified, that may be implemented in selected African countries (Tunisia, Kenya, Cameroon, Burkina Faso and South Africa); to this end, a procedure for assessing the potential adaptability to the local contexts (transferability audit) will be developed in WP7 and applied to promising interventions. Following a successful transferability audit, a detailed concept definition of the retained interventions will be made by SaferAfrica participants and local road safety experts. Furthermore, factsheets on five key challenging African safety issues will be developed as synthesised working documents, containing all technical and financial information necessary for understanding the corresponding set of proposed interventions...