California passes a new law aimed at shining a light on the growing number of crashes involving self-driving cars. Chances are, you have never actually ridden a scooter like this, zipping around corners, but you have definitely seen them weaving through city traffic. Just when you thought scooters were already a wild card on the road, imagine one that drives itself. That is exactly what the Omoway Omo X promises. Developed by a team of former Xpeng engineers, this scooter is not just electric, it is packed with smart features that push self-driving scooter tech to a whole new level, offering far more than you would ever expect from a two-wheeler.

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognitive states of both AV users and other road users, integrating this information into the AV's decision-making process. Specifically, our model captures the well-being of both an AV user and an interacting road user as cognitive states alongside trust. Our DBN models infer beliefs over the AV user's evolving well-being, trust, and intention states, as well as the possible well-being of other road users, based on observed interaction experiences. Using data collected from an interaction study, we refine the model parameters and empirically assess its performance. Finally, we extend our model into a causal inference model (CIM) framework for AV decision-making, enabling the AV to enhance user well-being and trust while balancing these factors with its own operational costs and the well-being of interacting road users. Our evaluation demonstrates the model's effectiveness in accurately predicting user's states and guiding informed, human-centered AV decisions.

Recent advances in Automated vehicle (AV) technology and micromobility devices promise a transformational change in the future of mobility usage. These advances also pose challenges concerning human-AV interactions. To ensure the smooth adoption of these new mobilities, it is essential to assess how past experiences and perceptions of social interactions by people may impact the interactions with AV mobility. This research identifies and estimates an individual's wellbeing based on their actions, prior experiences, social interaction perceptions, and dyadic interactions with other road users. An online video-based user study was designed, and responses from 300 participants were collected and analyzed to investigate the impact on individual wellbeing. A machine learning model was designed to predict the change in wellbeing. An optimal policy based on the model allows informed AV actions toward its yielding behavior with other road users to enhance users' wellbeing. The findings from this study have broader implications for creating human-aware systems by creating policies that align with the individual state and contribute toward designing systems that align with an individual's state of wellbeing.

Self-driving scooters could soon be whizzing around without riders in a tech development designed to improve cities' transport-sharing networks. A California-based start-up, Tortoise, is working on self-driving technology with which bikes and scooters drive themselves home after someone has used them. And taxi-hailing app Uber announced earlier in the year that it was working towards the same goal. Set to launch next month, the initiative is a progression for the temporary bike, scooter and Segway hire schemes which already exist around the world. It could mean fewer of the bikes and scooters are left lying around in obscure places and that they all return to charging stations or busy areas when not in use.

The KickScooter T60 is a new self-driving scooter that was introduced in China to drive itself to a charging station. Most countries are falling behind with the new technology, having to gather their scooters around the town every night for them to be charged. However, this new idea prevents all that work as it uses AI to navigate its way back to the charging stations without a person.

Yeah, WIRED Transportation loves new car tech. But this week, we were all about alternatives to driving around on four wheels. We thought about what might make public transit more comfortable for more than half of the population--you know, women. We wondered what might happen to the nation's aviation system now that the federal government is open again--and came away depressed. And we took a close look at Amazon's delivery robots.

In cities around the world, the spread of technology is giving rise to a unique problem – collisions between phone-obsessed walkers. But one group in Singapore believes it may have the answer in the form of a self-driving scooter. According to its makers, the vehicle could shuttle around people multitasking or mesmerised by their gadgets, helping to avoid collisions on the street. The one-seater, four wheel, 50 kg (110 lbs) vehicle travels at a top speed of 4mph (6 kmh) and has laser sensors to help navigate around obstacles. The scooter, developed by the National University of Singapore (NUS), is the city-state's latest experiment with driverless vehicles as it pushes ahead with its vision of using autonomous technology to help deal with the challenges of its limited land and labour.