Novel approaches for the safety of human-robot interaction

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 factor in wide scale adoption of personal robots, and until these issues are addressed, commercial enterprises will be unlikely to invest heavily in their development.In this thesis we argue that traditional system design techniques fail to capture the complexities associated with dynamic environments. This is based on a careful analysis of current design processes, which looks at how effectively they identify hazards that may arise in typical environments that a personal robot may be required to operate in. Based on this investigation, we show how the adoption of a hazard check list that highlights particular hazardous areas, can be used to improve current hazard analysis techniques.A novel safety-driven control system architecture is presented, which attempts to address many of the weaknesses identified with the present designs found in the literature. The new architecture design centres around safety, and the concept of a `safety policy' is introduced. These safety policies are shown to be an effective way of describing safety systems as a set of rules that dictate how the system should behave in potentially hazardous situations.A safety analysis methodology is introduced, which integrates both our hazard analysis technique and the implementation of the safety layer of our control system. This methodology builds on traditional functional hazard analysis, with the addition of processes aimed to improve the safety of personal robots. This is achieved with the use of a safety system, developed during the hazard analysis stage. This safety system, called the safety protection system, is initially used to verify that safety constraints, identified during hazard analysis, have been implemented appropriately. Subsequently it serves as a high-level safety enforcer, by governing the actions of the robot and preventing the control layer from performing unsafe operations.To demonstrate the effectiveness of the design, a series of experiments have been conducted using both simulation environments and physical hardware. These experiments demonstrate the effectiveness of the safety-driven control system for performing tasks safely, while maintaining a high level of availability

Autonomous Goods Vehicles for Last-mile Delivery:Evaluation of Impact and Barriers

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 the number of deliveries a driver can complete in a day. To further optimise this process, companies are starting to test autonomous goods vehicles (AGVs). This paper presents an evaluation of the impact and barriers of AGVs for last-mile delivery in the UK, by conducting a study of people in the logistics industry and experts in autonomous technology. Qualitative analysis is used to identify positive and negative impacts of the introduction of driverless AGVs, and barriers, in terms of government policy and technical restrictions, which could slow down wide-scale adoption. From the results, we find logistics companies are being pressured to reduce lead-times and offer more predictable delivery-times. This is increasing pressure on the workforce, which already has high-turnover and difficulties in recruitment. Therefore, AGVs are considered a solution to a present problem, which is preventing logistics companies growing and achieving delivery targets, driven by public demand.</p

An evolutionary approach to the optimisation of autonomous pod distribution for application in an urban transportation service

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 novel approach for distribution of AVs within an urban environment. Our approach uses evolutionary computation, in the form of a genetic algorithm (GA), which is applied to a simulation of an intelligent transportation service, operating in the city of Coventry, UK. The goal of the GA is to optimise distribution of pods, to reduce the amount of user waiting time. To test the algorithm, real-world transport data was obtained for Coventry, which in turn was processed to generate user demand patterns. Results from the study showed a 30% increase in the number of successful journeys completed in a 24 hours, compared to a random distribution. The implications of these findings could yield significant benefits for fleet management companies. These include increases in profits per day, a decrease in capital cost, and better energy efficiency. The algorithm could also be adapted to any service offering pick up and drop of points, including package delivery and transportation of goods

Millimeter-wave communication for a last-mile autonomous transport vehicle

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 throughput characteristics that 5G technology has to offer. This work presents an investigation of 5G millimeter-wave communication links for a low-speed autonomous vehicle, focusing on the effects of the antenna positions on both the received signal quality and the link performance. It is observed that the excess loss for communication with roadside infrastructure in front of the vehicle is nearly half-power beam width independent, and the increase of the root mean square delay spread plays a minor role in the resulting signal quality, as the absolute times are considerably shorter than the typical duration of 5G New Radio symbols. Near certain threshold levels, a reduction of the received power affects the link performance through an increased error vector magnitude of the received signal, and subsequent decrease of the achieved data throughput

Exploring the utility of EDA and skin temperature as individual physiological correlates of motion sickness

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 state, although, this is mainly measured after an MS exposure and not throughout exposure(s) to a MS task. A unique contribution of this paper is in the real-time tracking of subjective MS alongside real-time physiological measurements of Electrodermal Activity (EDA) and skin temperature. Data was collected in both simulator-based (controlled) and on-road (naturalistic) studies. 40 participants provided at total of 61 data sets, providing 1,603 minutes of motion sickness data for analysis. This study is in agreement that these measures are related to MS but evidenced a total lack of reliability for these measures at an individual level for both simulator and on-road experimentation. It is likely that other factors, such as environment and emotional state are more impactful on these physiological measures than MS itself. At a cohort level, the applicability of physiological measures is not considered useful for measuring MS accurately or reliably in real-time. Recommendations for further research include a mixed-measures approach to capture other data types (such as subject activity) and to remove contamination of physiological measures from environmental changes

A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field

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 window decreases. This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector field is incorporated as an additional input to a convolutional-recurrent deep neural network to help predict the most likely future trajectories given a sequence of bird's eye view scene representations. The performance of the proposed model is compared with state-of-the-art methods on the HighD dataset demonstrating that the VVF inclusion improves the prediction accuracy for both short and long-term (5~sec) time horizons. It is also shown that the accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction. Source codes are available at: https://github.com/Amir-Samadi/VVF-TP.Comment: This paper has been accepted and nominated as the best student paper at the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Exploring the impact of autonomous taxis on people with disabilities

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 traditional taxi experiences shape expectations of autonomous taxis in terms of disability accessibility? 2) To what extent does the autonomy of self-driving taxis contribute to a perceived increase in travel freedom? Thirty-two semi-structured interviews were conducted with administrative staff from disability organizations, and the perspectives of 39,079 organization members were included. Thematic and sentiment analyses were applied to analyse the findings, which revealed three main themes: onboarding, in-vehicle conditions, and offboarding. The absence of a driver was strongly correlated with a positive sentiment of increased travel freedom, indicating that autonomous taxis could provide enhanced accessibility without the limitations or biases associated with traditional taxis. Participants expressed concerns about driver attitudes and behaviour as negative experiences with traditional taxis. In contrast, with autonomous taxis, their main concern was the availability of human assistance to meet specific user needs throughout the journey. This study emphasizes the necessity for further research into the diverse and intricate spectrum of disabilities, as well as the importance of user-centric market research in the design process. Such research is crucial in achieving the overarching goal of improved accessibility

Using fNIRS to Verify Trust in Highly Automated Driving

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). Through manipulating participants’ expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems

A human factors approach to defining requirements for low-speed autonomous vehicles to enable intelligent platooning

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 the key areas of the system that could affect users and other stakeholders, such as businesses and the public. These initial findings were used to inform a series of focus groups, aimed at identifying the public's views of multiple autonomous vehicles being deployed in a pedestrianised area that can join and form platoons. Analysis of findings from the focus groups suggests that while people view platooning public transport vehicles favourably as a passenger, they have some concerns from a pedestrian perspective. Thematic analysis was applied to these findings and a systematic approach was used to identify where subjective outputs could be formalised to inform requirements. Finally, a step-by-step requirements elicitation process is presented that illustrates the method used to convert qualitative user data to objective engineering requirements

Review of graph-based hazardous event detection methods for autonomous driving systems

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 without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges