̽��ֱ�� of Cambridge - safety

360-degree head-up display view could warn drivers of road obstacles in real time

sc604 — Wed, 20 Dec 2023 06:00:26 +0000

Researchers have developed an augmented reality head-up display that could improve road safety by displaying potential hazards as high-resolution three-dimensional holograms directly in a driver’s field of vision in real time.

Using machine learning to monitor driver ‘workload’ could help improve road safety

sc604 — Thu, 07 Dec 2023 07:48:29 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, working in partnership with Jaguar Land Rover (JLR) used a combination of on-road experiments and machine learning as well as Bayesian filtering techniques to reliably and continuously measure driver ‘workload’. Driving in an unfamiliar area may translate to a high workload, while a daily commute may mean a lower workload.

̽��ֱ��resulting algorithm is highly adaptable and can respond in near real-time to changes in the driver’s behaviour and status, road conditions, road type, or driver characteristics.

This information could then be incorporated into in-vehicle systems such as infotainment and navigation, displays, advanced driver assistance systems (ADAS) and others. Any driver-vehicle interaction can be then customised to prioritise safety and enhance the user experience, delivering adaptive human-machine interactions. For example, drivers are only alerted at times of low workload, so that the driver can keep their full concentration on the road in more stressful driving scenarios. ̽��ֱ��results are reported in the journal IEEE Transactions on Intelligent Vehicles.

“More and more data is made available to drivers all the time. However, with increasing levels of driver demand, this can be a major risk factor for road safety,” said co-first author Dr Bashar Ahmad from Cambridge’s Department of Engineering. “There is a lot of information that a vehicle can make available to the driver, but it’s not safe or practical to do so unless you know the status of the driver.”

A driver’s status – or workload – can change frequently. Driving in a new area, in heavy traffic or poor road conditions, for example, is usually more demanding than a daily commute.

“If you’re in a demanding driving situation, that would be a bad time for a message to pop up on a screen or a heads-up display,” said Ahmad. “ ̽��ֱ��issue for car manufacturers is how to measure how occupied the driver is, and instigate interactions or issue messages or prompts only when the driver is happy to receive them.”

There are algorithms for measuring the levels of driver demand using eye gaze trackers and biometric data from heart rate monitors, but the Cambridge researchers wanted to develop an approach that could do the same thing using information that’s available in any car, specifically driving performance signals such as steering, acceleration and braking data. It should also be able to consume and fuse different unsynchronised data streams that have different update rates, including from biometric sensors if available.

To measure driver workload, the researchers first developed a modified version of the Peripheral Detection Task to collect, in an automated way, subjective workload information during driving. For the experiment, a phone showing a route on a navigation app was mounted to the car’s central air vent, next to a small LED ring light that would blink at regular intervals. Participants all followed the same route through a mix of rural, urban and main roads. They were asked to push a finger-worn button whenever the LED light lit up in red and the driver perceived they were in a low workload scenario.

Video analysis of the experiment, paired with the data from the buttons, allowed the researchers to identify high workload situations, such as busy junctions or a vehicle in front or behind the driver behaving unusually.

̽��ֱ��on-road data was then used to develop and validate a supervised machine learning framework to profile drivers based on the average workload they experience, and an adaptable Bayesian filtering approach for sequentially estimating, in real-time, the driver’s instantaneous workload, using several driving performance signals including steering and braking. ̽��ֱ��framework combines macro and micro measures of workload where the former is the driver’s average workload profile and the latter is the instantaneous one.

“For most machine learning applications like this, you would have to train it on a particular driver, but we’ve been able to adapt the models on the go using simple Bayesian filtering techniques,” said Ahmad. “It can easily adapt to different road types and conditions, or different drivers using the same car.”

̽��ֱ��research was conducted in collaboration with JLR who did the experimental design and the data collection. It was part of a project sponsored by JLR under the CAPE agreement with the ̽��ֱ�� of Cambridge.

“This research is vital in understanding the impact of our design from a user perspective, so that we can continually improve safety and curate exceptional driving experiences for our clients,” said JLR’s Senior Technical Specialist of Human Machine Interface Dr Lee Skrypchuk. “These findings will help define how we use intelligent scheduling within our vehicles to ensure drivers receive the right notifications at the most appropriate time, allowing for seamless and effortless journeys.”

̽��ֱ��research at Cambridge was carried out by a team of researchers from the Signal Processing and Communications Laboratory (SigProC), Department of Engineering, under the supervision of Professor Simon Godsill. It was led by Dr Bashar Ahmad and included Nermin Caber (PhD student at the time) and Dr Jiaming Liang, who all worked on the project while based at Cambridge’s Department of Engineering.

Reference:
Nermin Caber et al. ‘Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data.’ IEEE Transactions on Intelligent Vehicles (2023). DOI: 10.1109/TIV.2023.3313419

Researchers have developed an adaptable algorithm that could improve road safety by predicting when drivers are able to safely interact with in-vehicle systems or receive messages, such as traffic alerts, incoming calls or driving directions.

There is a lot of information that a vehicle can make available to the driver, but it’s not safe or practical to do so unless you know the status of the driver

Bashar Ahmad

Coneyl Jay via Getty Images

Head up display of traffic information and weather as seen by the driver

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Real-time drone intent monitoring could enable safer use of drones and prevent a repeat of 2018 Gatwick incident

sc604 — Tue, 14 Sep 2021 23:12:50 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, used a combination of statistical techniques and radar data to predict the flight path of a drone, and whether it intends to enter a restricted airspace, for instance around a civilian airport.

Their solution could help prevent a repeat of the Gatwick incident, as it can spot any drones before they enter restricted airspace and can determine, early, if their future actions are likely to pose a threat to other aircraft.

This new predictive capability can enable automated decision-making and significantly reduce the workload on drone surveillance system operators by offering actionable information on potential threats to facilitate timely and proportionate responses.

Real radar data from live drone trials at several locations was used to validate the new approach. Some of the results will be reported today (15 September) at the Sensor Signal Processing for Defence Conference in Edinburgh.

Drones have become ubiquitous over the past several years, with widespread applications in agriculture, surveying and e-commerce, among other fields. However, they can also be a nuisance or present a potential safety risk, especially with the wide availability of cheap and increasingly more capable platforms.

A few days before Christmas 2018, reported drone sightings near the perimeter of Gatwick Airport caused hundreds of flights to be disrupted due to the possible risk of collision. No culprit was found.

“While we don’t fully know what happened at Gatwick, the incident highlighted the potential risk drones can pose to the public if they are misused, whether that’s done maliciously or completely innocently,” said paper co-author Dr Bashar Ahmad, who carried out the research while based at Cambridge’s Department of Engineering. “It’s crucial for future drone surveillance systems to have predictive capabilities for revealing, as early as possible, a drone with malicious intent or anomalous behaviour.”

To aid with air traffic control and prevent any possible collisions, commercial airplanes report their location every few minutes. However, there is no such requirement for drones.

“There needs to be some sort of automated equivalent to air traffic control for drones,” said Professor Simon Godsill from Cambridge’s Department of Engineering, who led the project. “But unlike large and fast-moving targets, like a passenger jet, drones are small, agile, and slow-moving, which makes them difficult to track. They can also easily be mistaken for birds, and vice versa.”

“We need to spot threats as early as possible, but we also need to be careful not to overreact, since closing civilian airspace is a drastic and highly disruptive measure that we want to avoid, especially if it ends up being a false alarm,” said first author Dr Jiaming Liang, also from the Department of Engineering, who developed the underlying algorithms with Godsill.

There are several potential ways to monitor the space around a civilian airport. A typical drone surveillance solution can use a combination of several sensors, such as radar, radio frequency detectors and cameras, but it’s often expensive and labour-intensive to operate.

Using Bayesian statistical techniques, the Cambridge researchers built a solution that would only flag those drones which pose a threat and offer a way to prioritise them. Threat is defined as a drone that’s intending to enter restricted airspace or displays an unusual flying pattern.

“We need to know this before it happens, not after it happens,” said Godsill. “This way, if a drone is getting too close, it could be possible to warn the drone operator. For obvious safety reasons, it’s prohibited to disable a drone in civilian airspace, so the only option is to close the airspace. Our goal is to make sure airport authorities don’t have to do this unless the threat is a real one.”

̽��ֱ��software-based solution uses a stochastic, or random, model to determine the underlying intent of the drone, which can change dynamically over time. Most drones navigate using waypoints, meaning they travel from one point to the next, and a single journey is made of multiple points.

In tests using real radar data, the Cambridge-developed solution was able to identify drones before they reached their next waypoint. Based on a drone’s velocity, trajectory and other data, it was able to predict the probability of any given drone reaching the next waypoint in real time.

“In tests, our system was able to spot potential threats in seconds, but in a real scenario, those seconds or minutes can make the difference between an incident happening or not,” said Liang. “It could give time to warn incoming flights about the threat so that no one gets hurt.”

̽��ֱ��Cambridge researchers say their solution can be incorporated into existing surveillance systems, making it a cost-effective way of tracking the risk of drones ending up where they shouldn’t. ̽��ֱ��algorithms could, in principle, also be applied to other domains such as maritime safety, robotics and self-driving cars.

Reference:
Jiaming Liang et al. ‘Detection of Malicious Intent in Non-cooperative Drone Surveillance.’ Paper presented at the Sensor Signal Processing for Defence conference. Edinburgh, UK. 14-15 September 2021. https://sspd.eng.ed.ac.uk/

Researchers have developed a real-time approach that can help prevent incidents like the large-scale disruption at London’s Gatwick Airport in 2018, where possible drone sightings at the perimeter of the airport caused the cancellation of hundreds of flights.

While we don’t fully know what happened at Gatwick, the incident highlighted the potential risk drones can pose to the public if they are misused

Bashar Ahmad

Goh Rhy Yan via Unsplash

Drone and city skyline

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Keeping patients safe in hospital

cjb250 — Tue, 15 Nov 2016 09:39:09 +0000

In November 2004, Mary McClinton was admitted to Virginia Mason Medical Center in Seattle, USA, to receive treatment for a brain aneurysm, a potentially serious swelling in a blood vessel. What followed was a tragedy, made worse by the fact that it was entirely preventable.

McClinton was mistakenly injected with the antiseptic chlorhexidine. It happened, the hospital says, because of “confusion over the three identical stainless steel bowls in the procedure room containing clear liquids — chlorhexidine, contrast dye and saline solution”. Doctors tried amputating one of her legs to save her life, but the damage to her organs was too great: McClinton died 19 days later.

Nine years on, an almost identical accident occurred at Doncaster Royal Infirmary in the UK. Here, the patient, ‘Gina’, survived, but only after having her leg amputated.

Professor Mary Dixon-Woods is one of Cambridge’s newest recruits, and she is on a mission: to improve patient safety in the National Health Service and in healthcare worldwide. She has recently taken up the role as RAND Professor of Health Services Research, having moved here from the ̽��ֱ�� of Leicester.

It is, she admits, going to be a challenge. Many different policies and approaches have been tried to date, but few with widespread success, and often with unintended consequences.

Financial incentives are widely used in the NHS and in the USA, but recent evidence suggests that they have little effect. “There’s a danger that they tend to encourage effort substitution – what people often refer to as ‘teaching to the test’,” explains Dixon-Woods. In other words, people focus on the areas that are being incentivised, but neglect other areas. “It’s not even necessarily conscious neglect. People have only a limited amount of time, so it’s inevitable they focus on areas that are measured and rewarded: it’s an economy of attention as much as anything else.”

In 2013, Dixon-Woods and colleagues published a study, funded by the Wellcome Trust, evaluating the use of surgical checklists introduced in hospitals to reduce complications and deaths during surgery. ̽��ֱ��checklists have become the most widely used patient safety intervention in the world and are recommended by the World Health Organization. Yet, the evidence shows that checklists may have little impact, and her research found that in some situations – particularly in low-income countries – they might even make things worse.

“ ̽��ֱ��checklists sometimes introduced new risks. Nurses would use the lists as a box-ticking exercise rather than as a true reflection of events – they would tick the box to say the patient had had their antibiotics when there were no antibiotics in the hospital, for example.” They also reinforced the hierarchies – nurses had to try to get surgeons to do certain tasks, but the surgeons used it as an opportunity to display their power and refuse.

Problems are compounded by a lack of standardisation. Dixon-Woods and her team spend time in hospitals to try to understand which systems are in place and how they are used. Not only does she find differences in approaches between hospitals, but also between units and even between shifts. “Standardisation and harmonisation are two of the most urgent issues we have to tackle. Imagine if you have to learn each new system wherever you go or even whenever a new senior doctor is on the ward. This introduces massive risk.”

One place that has managed to break this pattern is Northern Ireland, which has overcome the problem of poor labelling of lines such as intravenous lines and urinary catheters

Even when an institution manages to make genuine improvements in patient safety, too often these interventions cannot be replicated elsewhere or scaled up, leading to the curse of “worked once”, as she describes it.

One place that has managed to break this pattern is Northern Ireland, which has overcome the problem of poor labelling of lines such as intravenous lines and urinary catheters. A sick patient may have several different lines attached to them; these were not labelled in any consistent way – if at all – so a nurse might use the wrong line or leave a line in place too long, risking infection. Over 18 months, the health service in Northern Ireland came up with a solution. Soon, whether you are in a hospital, a nursing home or a hospice, every line will be labelled the same way.

“I’m interested in how they managed to achieve that and what we can learn that can be used in the next place that wants to standardise their lines.”

Dixon-Woods compares the issue of patient safety to that of climate change, in the sense that it is a “problem of many hands”, with many actors, each making a contribution towards the outcome, and where it is difficult to identify who has responsibility for solving the problem. “Many patient safety issues arise at the level of the system as a whole, but policies treat patient safety as an issue for each individual organisation.”

Nowhere is this more apparent than the issue of ‘alarm fatigue’. Each bed in an intensive care unit typically generates 160 alarms per day, caused by machinery that is not integrated. “You have to assemble all the kit around an intensive care bed manually,” she explains. “It doesn’t come built as one like an aircraft cockpit. This is not a problem a hospital can solve alone. It needs to be solved at the sector level.”

Dixon-Woods has turned to Professor John Clarkson in Cambridge’s Engineering Design Centre to help. Clarkson has been interested in patient safety for over a decade; in 2004, his team published a report for the Chief Medical Officer entitled ‘Design for patient safety – a system-wide design-led approach to tackling patient safety in the NHS’.

We need to look through the eyes of the healthcare providers to see the challenges and to understand where tools and techniques we use in engineering may be of value
John Clarkson

“Fundamentally, my work is about asking how can we make it better and what could possibly go wrong,” explains Clarkson. It is not, he says, just about technology, but about the system and the people within the system. When he trains healthcare professionals, he avoids using words like ‘risk’, which mean different things in medicine and engineering, and instead asks questions to get them thinking about the system.

“We need to look through the eyes of the healthcare providers to see the challenges and to understand where tools and techniques we use in engineering may be of value. I have no doubt that if you were to put a hundred engineers into Addenbrooke’s [Hospital], you could help transform its care.”

There is a difficulty, he concedes: “There’s no formal language of design in healthcare. Do we understand what the need is? Do we understand what the requirements are? Can we think of a range of concepts we might use and then design a solution and test it before we put it in place? We seldom see this in healthcare, and that’s partly driven by culture and lack of training, but partly by lack of time.”

Dixon-Woods agrees that healthcare can learn much from how engineers approach problems. “Medical science tends to prioritise trials and particular types of evidence, whereas engineering does rapid tests. Randomised controlled trials do have a vital role, but on their own they’re not the whole solution. There has to be a way of getting our two sides talking.”

Only then, she says, will we be able to prevent further tragedies such as the death of Mary McClinton.

Healthcare is a complex beast and too often problems arise that can put patients’ health – and in some cases, lives – at risk. A collaboration between the Cambridge Centre for Health Services Research and the Department of Engineering hopes to get to the bottom of what’s going wrong – and to offer new ways of solving the problems.

Standardisation and harmonisation are two of the most urgent issues we have to tackle. Imagine if you have to learn each new system wherever you go or even whenever a new senior doctor is on the ward. This introduces massive risk

Mary Dixon-Woods

Toshiyuki Imai

Intravenous drip

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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