Mobility is a central issue in the transition to a more sustainable lifestyle. The average daily distance traveled by the French population has increased considerably, from 5 km on average in the 1950s to 45 km on average in 2011 [58], as has the number of personal cars (11,860 million cars in 1970 [7] compared to 38,3 million in 2021 [15, 28]). For example in Toulouse, cars concentrate 74% of the distances traveled by the inhabitants and contribute up to 88% to GHG emissions [25]. The evolution of mobility is therefore an essential question, both for the global climate crisis and for public health: negative impact of a sedentary lifestyle [9], road accidents, air and sound pollution [44]. Indeed, 40000 deaths per year are attributable to exposure to fine particles (PM2.5) and 7000 deaths per year attributable to exposure to nitrogen dioxide (NO2), i.e. 7% and 1% of the total annual mortality [38]; the 2-month lockdown of spring 2020 in France saved 2300 deaths by reducing exposure to particles, and 1200 more deaths by reducing exposure to nitrogen dioxide [38].

Next generation cars embed intelligent assessment of car driving safety through innovative solutions often based on usage of artificial intelligence. The safety driving monitoring can be carried out using several methodologies widely treated in scientific literature. In this context, the author proposes an innovative approach that uses ad-hoc bio-sensing system suitable to reconstruct the physio-based attentional status of the car driver. To reconstruct the car driver physiological status, the author proposed the use of a bio-sensing probe consisting of a coupled LEDs at Near infrared (NiR) spectrum with a photodetector. This probe placed over the monitored subject allows to detect a physiological signal called PhotoPlethysmoGraphy (PPG). The PPG signal formation is regulated by the change in oxygenated and non-oxygenated hemoglobin concentration in the monitored subject bloodstream which will be directly connected to cardiac activity in turn regulated by the Autonomic Nervous System (ANS) that characterizes the subject's attention level. This so designed car driver drowsiness monitoring will be combined with further driving safety assessment based on correlated intelligent driving scenario understanding.

With the accelerating evolution of technology, artificial intelligence (AI) plays a growing role in decision-making processes. Humans are becoming increasingly dependent on algorithms to process information, recommend certain behaviors, and even take actions of their behalf. A research team has studied how humans react to the introduction of AI decision making. Specifically, they explored the question, 'is society ready for AI ethical decision making?' by studying human interaction with autonomous cars.

In shared spaces, motorized and non-motorized road users share the same space with equal priority. Their movements are not regulated by traffic rules, hence they interact more frequently to negotiate priority over the shared space. To estimate the safeness and efficiency of shared spaces, reproducing the traffic behavior in such traffic places is important. In this paper, we consider and combine different levels of interaction between pedestrians and cars in shared space environments. Our proposed model consists of three layers: a layer to plan trajectories of road users; a force-based modeling layer to reproduce free flow movement and simple interactions; and a game-theoretic decision layer to handle complex situations where road users need to make a decision over different alternatives. We validate our model by simulating scenarios involving various interactions between pedestrians and cars and also car-to-car interaction. The results indicate that simulated behaviors match observed behaviors well.

Self-driving cars can be as stubborn as a mule. Sometimes it seems as though a car is about as stubborn as a mule or perhaps acting bull-headed. Here's an example of something I witnessed first-hand the other day. A tow truck was getting ready to take a car for a tow. This was a flatbed style tow truck. You've surely seen these types of tow trucks on the roadways wherein they piggyback a car that needs to be transported. The tail end of the flatbed portion tilts at a somewhat acute angle to allow for driving a car up onto the riding platform. This forms a ramp for the car to traverse upward onto the empty and awaiting flatbed area.

Modern cars and their often clunky interactive screens are getting a revamp, courtesy of artificial intelligence experts at the University of Cambridge. The new system works by tracking a user's finger in mid-air and does not need them to physically touch the screen in order to select an option. Instead, an array of sensors, cameras and AI will identify the finger and work out what it is pointing at. By removing the need for physical contact, it could also offer a sterile option in cars as we enter the post-Covid world. There has been no announcement yet as to when the first functioning no-touch screens will be fitted to vehicles and available for purchase.

Being from California, I remember one of the first times that I visited New York City (NYC) and made the mistake of renting a car to get around the famous metropolis. I had figured that driving a car around the avenues and streets would give me a good sense of how the city that never sleeps was laid out and where the most notable restaurants, bars, and shops could be found. Turns out that I mainly discovered how much New Yorkers seemed to delight in jaywalking. It was as though there weren't any rules against jaywalking. Want to cut across the street and get over to that popular hangout, no need to walk down to a crosswalk, instead just make your way by walking into traffic. In most cases, the jaywalker didn't even run. One might almost think that you would dart rather than meander, but these fearless jaywalkers tended to take their time. I also found out about the techniques involved in making a devoted stare or gaze that appeared to be a local custom. In some cities, the jaywalker purposely does not make eye contact with the car drivers, seemingly acting as though the car drivers don't exist. Or, maybe by making eye contact it would become a duel to see who looked away first, and the loser perhaps has to back-down from the standoff. In any case, my experience was that the jaywalkers in NYC loved to give the car drivers a straight eye. This might be the same kind of thing you'd do when you encounter a wild animal in the woods. Given them a strong stare might say that you are mighty and the animal should not try to take you on. Some of the car drivers that were locals or that were used to the local customs would often give a stern stare back. On a few occasions, it would get really testy and the jaywalker would wave an arm and act as though they might try to slay the dragon of a car coming down the street. I admit that after I turned in the rental car and became more of a traditional pedestrian on my visits to NYC, I adopted the jaywalking habit. This was especially so because during one of my initial forays as a pedestrian there, I was walking with a colleague that was a native New Yorker, and when I attempted to walk down to a crosswalk, rather than taking the shortcut of jaywalking, he almost came out of his skin at my legal abiding approach. Are you nuts, he asked or demanded incredulously?

While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more pervasive, but one of its key limitations lies in its inconsistent reliability stemming from the data contributed by unreliable human sensors. In this paper, we present DASC, a road Damage-Aware Social-media-driven Car sensing framework that exploits the collective power of social sensing and VSNs for reliable disaster response applications. However, integrating VSNs with social sensing introduces a new set of challenges: i) How to leverage noisy and unreliable social signals to route the vehicles to accurate regions of interest? ii) How to tackle the inconsistent availability (e.g., churns) caused by car drivers being rational actors? iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media? The DASC framework addresses the above challenges by establishing a novel hybrid social-car sensing system that employs techniques from game theory, feedback control, and Markov Decision Process (MDP). In particular, DASC distills signals emitted from social media and discovers the road damages to effectively drive cars to target areas for verifying emergency events. We implement and evaluate DASC in a reputed vehicle simulator that can emulate real-world disaster response scenarios. The results of a real-world application demonstrate the superiority of DASC over current VSNs-based solutions in detection accuracy and efficiency.

NASCAR race when fans were in the seats, which will happen again soon, and meanwhile let's consider ... [ ] adding self-driving cars to the mix too. "Drivers, start your engines" was the battle cry this weekend. NASCAR racing is back underway after a ten-week halt due to the pandemic, and Sunday's winner was long-time race car driver Kevin Harvick, also known as The Closer or Happy Harvick. He certainly did the closing on Sunday and indubitably seemed quite happy with the outcome. There were forty race cars and each of the 40 drivers expressed elation to be racing once again after the lengthy furlough.

A research team at Ben-Gurion University have created a simple projection system able to trick Tesla's Autopilot into seeing things that aren't actually there. Using commercially available drones and a cheap projector - the kind a person might use to watch television in an apartment of small home - the team projected a series of deceptive images onto the road. The images included false traffic lines, a false speed limit sign, and an image of Elon Musk himself, projected on the road as if her were an endangered pedestrian. The researchers collectively labeled all these different visual phenomena as'phantoms,' according to a report in ArsTechnica. While the Tesla they tested reacted to every phantom in some way, most of its responses were fairly mild.