If, like me, you suffer from motion sickness, then you know just how quickly a trip down Britain's winding back roads can turn into a nausea-inducing nightmare. But if you struggle to hold on to your lunch as the car starts to lurch, there may soon be a solution. ClearMotion, a Boston-based startup, claims that its latest generation of cutting-edge suspension can'eliminate motion sickness' for good. So, with anti-nausea tablets in hand, MailOnline's reporter, Wiliam Hunter, took a trip to their Warwickshire testing facility to try it for himself. With compact motors tucked away above each wheel and a sophisticated onboard computer, the system can push and pull the wheels to cancel out bumps in the road.

In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associated infrastructure. At that point, a Digital Twin becomes particularly appealing because, unlike a basic simulation, it offers a continuous, bilateral connection linking the real and virtual environments. This paper describes the digital reconstruction used to develop the Digital Twin of the Test Area Autonomous Driving-Baden-Württemberg (TAF-BW), Germany. The TAF-BW offers a variety of different road sections, from high-traffic urban intersections and tunnels to multilane motorways. The test area is equipped with a comprehensive Vehicle-to-Everything (V2X) communication infrastructure and multiple intelligent intersections equipped with camera sensors to facilitate real-time traffic flow monitoring. The generation of authentic data as input for the Digital Twin was achieved by extracting object lists at the intersections. This process was facilitated by the combined utilization of camera images from the intelligent infrastructure and LiDAR sensors mounted on a test vehicle. Using a unified interface, recordings from real-world detections of traffic participants can be resimulated. Additionally, the simulation framework's design and the reconstruction process is discussed. The resulting framework is made publicly available for download and utilization at: https://digit4taf-bw.fzi.de The demonstration uses two case studies to illustrate the application of the digital twin and its interfaces: the analysis of traffic signal systems to optimize traffic flow and the simulation of security-related scenarios in the communications sector.

From the Ford Mustang to the Ford Focus, Ford has some of the most popular cars in the world. But Ford drivers be warned - your car could soon turn against you. That's according to a newly discovered patent, which suggests Ford is working on a way to allow your car to snitch on you for speeding. Among these challenges is a need to quickly and accurately identify a speeding vehicle and take responsive action,' the patent description reads. 'It is desirable to provide systems and methods that assist traffic police and/or other law enforcement officers perform such tasks.'

3D object detection plays a crucial role in various applications such as autonomous vehicles, robotics and augmented reality. However, training 3D detectors requires a costly precise annotation, which is a hindrance to scaling annotation to large datasets. To address this challenge, we propose a weakly supervised 3D annotator that relies solely on 2D bounding box annotations from images, along with size priors. One major problem is that supervising a 3D detection model using only 2D boxes is not reliable due to ambiguities between different 3D poses and their identical 2D projection. We introduce a simple yet effective and generic solution: we build 3D proxy objects with annotations by construction and add them to the training dataset. Our method requires only size priors to adapt to new classes. To better align 2D supervision with 3D detection, our method ensures depth invariance with a novel expression of the 2D losses. Finally, to detect more challenging instances, our annotator follows an offline pseudo-labelling scheme which gradually improves its 3D pseudo-labels. Extensive experiments on the KITTI dataset demonstrate that our method not only performs on-par or above previous works on the Car category, but also achieves performance close to fully supervised methods on more challenging classes. We further demonstrate the effectiveness and robustness of our method by being the first to experiment on the more challenging nuScenes dataset. We additionally propose a setting where weak labels are obtained from a 2D detector pre-trained on MS-COCO instead of human annotations.

Taking your hands off the steering wheel and feet off the pedals while travelling at 70mph might sound like a nightmare. The car contains a system called BlueCruise, which is the UK's only legal hands-free driving technology. But would you feel safe letting a car take over for you on the motorway? Ford loaned MailOnline's Shivali Best a Mustang Mach-E for the weekend so she could find out. Taking your hands off the steering wheel and feet off the pedals while travelling at 70mph might sound like a nightmare.

Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is mostly due to architectural design choices, which were often adopted from the 2D image domain, where geometric context is rarely available. In 3D, however, considering the object properties and its surroundings in a holistic way is important to distinguish between true and false positive detections, e.g. occluded pedestrians in a group. To address this, we present GACE, an intuitive and highly efficient method to improve the confidence estimation of a given black-box 3D object detector. We aggregate geometric cues of detections and their spatial relationships, which enables us to properly assess their plausibility and consequently, improve the confidence estimation. This leads to consistent performance gains over a variety of state-of-the-art detectors. Across all evaluated detectors, GACE proves to be especially beneficial for the vulnerable road user classes, i.e. pedestrians and cyclists.

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds) collected from the end-users' environments (i.e. target domain) to adapt the system to the difference between training and testing environments. While extensive research has been done on such an unsupervised domain adaptation problem, one fundamental problem lingers: there is no reliable signal in the target domain to supervise the adaptation process. To overcome this issue we observe that it is easy to collect unsupervised data from multiple traversals of repeated routes. While different from conventional unsupervised domain adaptation, this assumption is extremely realistic since many drivers share the same roads. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain. Concretely, we generate pseudo-labels with the out-of-domain detector but reduce false positives by removing detections of supposedly mobile objects that are persistent across traversals. Further, we reduce false negatives by encouraging predictions in regions that are not persistent. We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving.

A robot that uses the same technology as self-driving vehicles is transporting medicines around hospitals as part of a new trial. The'helper bot' is being used to carry and deliver prescriptions and other items around Milton Keynes University Hospital, helping to relieve pressure on human staff. It is the creation of British firm Academy of Robotics, which has already worked on autonomous technology for its'Kar-Go' self-driving vehicle. Just like Kar-Go, the bot uses sonar and LiDAR technology to navigate around obstacles such as people, wheelchairs and beds inside the hospital. The robot uses a combination of three types of sensors to see both into the distance and to understand how close objects are and how they are moving in relation to its own path.