Roger Woodman

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Verified

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• PhD
• Professor (Associate) at University of Warwick

Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate wit...

The automotive industry is undergoing profound changes, driven by the need for safer, more environmentally friendly, and more accessible future mobility and transport systems for goods and people. Enabling technologies include electrification, digitalisation, and automation of future vehicles. These technologies are powered by a multitude of onboar...

Automated vehicles (AVs) are being deployed in public traffic, which brings not only potential improvements in safety and efficiency but also new challenges for human drivers. This study investigates the effect of trait driving anger and varying time gaps between AVs and human-driven vehicles (HVs) in terms of drivers' driving performance and level...

The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS). Yet, such models are prone to errors which can have serious safety implications. Introspection and self-assessment models that aim to detect such errors are therefore of paramount importance for the safe deployment of ADS. Current research...

Early and accurate hazard detection is crucial for enhancing the safety of automated driving systems (ADS) by allowing them to anticipate and react to imminent accidents. While current research focuses on classifying safe and hazardous scenes, simply identifying a hazard class does not provide sufficient information for an appropriate response. Thi...

Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors, particularly in the less-explored realm of 3D object detection, remains a...

General-purpose motion planners for automated/autonomous vehicles promise to handle the task of motion planning (including tactical decision-making and trajectory generation) for various automated driving functions (ADF) in a diverse range of operational design domains (ODDs). The challenges of designing a general-purpose motion planner arise from...

Motion planning is an essential element of the modular architecture of autonomous vehicles, serving as a bridge between upstream perception modules and downstream low-level control signals. Traditional motion planners were initially designed for specific Automated Driving Functions (ADFs), yet the evolving landscape of highly automated driving syst...

Reliable detection of various objects and road users in the surrounding environment is crucial for the safe operation of automated driving systems (ADS). Despite recent progresses in developing highly accurate object detectors based on Deep Neural Networks (DNNs), they still remain prone to detection errors, which can lead to fatal consequences in...

Reliable and early detection of hazardous events is vital for the safe deployment of automated driving systems. Yet, it remains challenging as road environments can be highly complex and dynamic. State-of-the-art solutions utilise neural networks to learn visual features and temporal patterns from collision videos. However, in this paper, we show h...

The National Wellbeing Programme in the United Kingdom (UK) transcends traditional economic metrics, aiming to assess the nation's comprehensive performance. This study investigates the intricate relationship between transport policies, subjective wellbeing, and their implications for individuals with disabilities within the UK context. It conducts...

Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based...

The emerging development of smart sensing technology advances consumer electronics (CE) to support next generation of intelligent transportation. The artificial intelligence (AI)-based ambient intelligence and human-machine interaction are now able to acquire the (i) environment data from wireless sensing technologies, including GPS, Beidou, gyrosc...

Despite the significant benefits of Autonomous Vehicles (AVs) for global transportation, Indonesia's diverse geographical landscape encounter unique adoption challenges due to infrastructural shortcomings and economic limitations. This study explores the adoption of AVs in Indonesia, considering their potential market and crucial role in AV and Ele...

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This data is then processed by a perception subsystem to create semantic knowledge of the world around the vehicle. State-of-the-art ADSs’ perception systems often use d...

The implementation of fully autonomous vehicles (FAVs) has been proposed to yield societal and environmental benefits. However, there are uncertainties regarding the factors influencing the acceptance of FAVs in developing countries which has been understudied. Therefore, this study aims to examine the factors affecting public acceptance of the ado...

The integration of autonomous vehicles (AVs) into modern transportation systems is an inevitable development, highlighting the need to explore the factors influencing public acceptance of AVs. Although numerous studies have examined user acceptance of AVs in developed nations, the investigation in developing countries remains significantly limited....

While deep neural network (DNN) models have become extremely popular for object detection in automated driving systems (ADS), the dynamic and varied nature of the road traffic environment can still lead to model failures. To address this issue, researchers have recently explored introspection mechanisms, a.k.a, self-assessment, for monitoring the q...

Over the past two decades, transportation has become more accessible, but people with disabilities still face significant barriers to accessing these services. This research focuses on the impact of autonomous taxis on people with disabilities, an area that has seen limited improvement. The study aims to answer two research questions: 1) How do tra...

Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporary learning-based approaches for motion prediction exhibit significant performance degradation as the prediction horizon increases or the observation win...

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving. Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a...

Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver’s performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver...

Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Thr...

The driving domain is inherently dangerous. To develop connected and automated vehicles that can detect potential sources of harm, we must clearly define these hazardous events and metrics to detect them. The majority of driving scenarios we face do not materialise harm, but we often face potentially hazardous near-miss scenarios. Potential harm is...

While it is widely agreed that automated and autonomous vehicles may provide safety benefits over vehicles with lower level or no automation, due to other road users there will still likely be situations where a collision is unavoidable. What should a vehicle that is operating autonomously do when it has no choice but to have a collision? And who s...

Highly Automated Driving technology will be facing major challenges before being pervasively integrated across production vehicles. One of them will be monitoring drivers' state and determining whether they are ready to take over control under certain circumstances. Thus, we have explored their physiological responses and the effects on trust of di...

Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers' availability to take-over manual control in safety-critical scenarios. In this empirical researc...

Driver state monitoring (DSM) systems aim to measure driver/occupant state, considering factors such as fatigue, workload, attentiveness, and wellbeing. They are influential for some vehicles on the road today, but as we move towards higher levels of automation their use is expected to become even more important. Uncertainty around public perceptio...

Interest from industry and policy makers on the use of autonomous goods vehicles (AGVs) for commercial use, has increased rapidly as the underlying technology has developed. This study looks at the potential impact of autonomous road haulage on logistics operations and barriers for commercial implementation in the UK. The research focuses on large...

Almost everyone can experience motion sickness and one third of the population are highly susceptible. With growing development and popularity of technologies such as self-driving cars, simulators and virtual reality (VR), motion sickness management will be more of a consideration in the future than ever before. People who are susceptible to motion...

Motion sickness (MS) is known to be a potentially limiting factor for future self-driving vehicles – specifically in regards to occupant comfort and well-being. With this as a consideration comes the desire to accurately measure, track and even predict MS state in real-time. Previous research has considered physiological measurements to measure MS...

This paper conducts a simulation study of low-speed Autonomous Vehicles (AVs), referred to as “pods”, platooning in shared urban environments. The proposed on-demand transport service can help solve the “last mile” challenge and improve mobility for non-drivers, elderly, and disabled people. To help the industry understands the dynamic system for d...

For transport logistics, often the most inefficient part of the journey is the route between distribution centre and end customer. This route, referred to as last-mile delivery, generally uses smaller goods vehicles, to deliver low-volumes to multiple destinations. To optimise this process, route planning optimisation software is used, to maximise...

Text files expressing the Excess Loss (Fig. 3, left) and root mean square Delay Spread (Fig. 3, right) data arranged by azimuthal angle for the specified transmitting directive horn antenna and height. Text files expressing the Error Vector Magnitude (Fig. 4, left) and Throughput (Fig. 4, right) data arranged by azimuthal angle for the specified t...

Low-speed autonomous transport of passengers and goods is expected to have a strong, positive impact on the reliability and ease of travelling. Various advanced functions of the involved vehicles rely on the wireless exchange of information with other vehicles and the roadside infrastructure, thereby benefitting from the low latency and high throug...

Autonomous vehicles (AVs) operating in shared urban environments, often referred to as “pods”, will constantly have to interact with pedestrians. As a result, an effective strategy will be required for pods to continue operating, while in close proximity to people. This strategy could be in terms of active negotiation, where a pod identifies a pers...

For autonomous vehicles (AVs), which when deployed in urban areas are called "pods'", to be used as part of a commercially viable low-cost urban transport system, they will need to operate efficiently. Among ways to achieve efficiency, is to minimise time vehicles are not serving users. To reduce the amount of wasted time, this paper presents a nov...

This paper presents results from a series of focus groups, aimed at enhancing technical engineering system requirements, for a public transport system, encompassing a fleet of platooning low-speed autonomous vehicles (LSAV; aka pods) in urban areas. A critical review of the pods was conducted, as part of a series of technical workshops, to examine...

Face recognition has received much interest in the last decade, as the need for reliable personal identification security has become ever more critical. At present for face recognition to be a viable personal identification method an accurate low cost solution is required. Many two-dimensional (2D) face recognition systems have been implemented, wh...

Robot manufacturers will be required to demonstrate objectively that all reasonably foreseeable hazards have been identified in any robotic product design that is to be marketed commercially. This is problematic for autonomous mobile robots because conventional methods, which have been developed for automatic systems do not assist safety analysts i...

In recent years there has been a concerted effort to address many of the safety issues associated with physical human-robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. We believe that the safety issue is a primary fa...

In recent years there has been a concerted effort to address many of the safety issues associated with physical human–robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. In this paper we argue that traditional system d...

This paper presents a novel approach for designing robotic systems. The methodology aims to build on traditional functional hazard analysis, with the addition of processes aimed to improve the safety of autonomous personal robots. This will be achieved with the use of a safety protection system, developed during the hazard analysis stage. This prot...

This paper presents a novel robot control architecture for use with personal robots, and argues its potential for improving the safety of these types of system, when compared to existing approaches. The proposed architecture design separates the control system into two distinct areas, one area responsible for safe operation and the other for coordi...