Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving. A particular challenge in this endeavour are situations in which the vehicle's automation is no longer able to drive and is thus requesting the human to take over. In these situations the driver has to quickly build awareness for the traffic situation to be able to take over control and safely drive the car. Within this context we present a software and hardware framework to asses how aware the driver is about the situation and to provide human-centred assistance to help in building situation awareness. The framework is developed as a modular system within the Robot Operating System (ROS) with modules for sensing the environment and the driver state, modelling the driver's situation awareness, and for guiding the driver's attention using specialized Human Machine Interfaces (HMIs). A particular focus of this paper is on an Answer Set Programming (ASP) based approach for modelling and reasoning about the driver's interpretation and projection of the scene. This is based on scene data, as well as eye-tracking data reflecting the scene elements observed by the driver. We present the overall application and discuss the role of semantic reasoning and modelling cognitive functions based on logic programming in such applications. Furthermore we present the ASP approach for interpretation and projection of the driver's situation awareness and its integration within the overall system in the context of a real-world use-case in simulated as well as in real driving.

A quick-service restaurant chain is running its AI models on machines inside its stores to localize delivery logistics. At the same time, a global pharma company is training its machine learning models on premises, using servers it manages by itself. Cloud computing isn't going anywhere, but some companies that use machine learning models and the tech vendors supplying the platforms to manage them say machine learning is having an on-premises moment. For many years, cloud providers have argued that the computing requirements for machine learning would be far too expensive and cumbersome to start up on their own, but the field is maturing. "We still have a ton of customers who want to go on a cloud migration, but we're definitely now seeing -- at least in the past year or so -- a lot more customers who want to repatriate workloads back onto on-premise because of cost," said Thomas Robinson, vice president of strategic partnerships and corporate development at MLOps platform company Domino Data Lab.

The global automotive artificial intelligence market is expected to grow at a CAGR of 39.8% from 2019 to reach $15.90 billion by 2027 with Asia Pacific automotive artificial intelligence market expected to grow at the highest CAGR. In modern digital society, artificial intelligence is becoming a dynamic business norm. The adoption of automotive artificial intelligence is routing in a new era allowing the companies to track their operations, augment business strategies, enhance in-car user experience, develop autonomous and semi-autonomous vehicles, and provide a better outcome in the digital world. The growth of the global automotive artificial intelligence market is primarily driven by factors, such as growing demand for autonomous vehicles, adoption of advanced automotive solutions, growing adoption of artificial intelligence for traffic management, government initiatives and investments in connected and autonomous vehicles. However, the lack of infrastructure coupled with the high procurement and operational costs are the major hindrances to market growth. The global market for autonomous vehicles has witnessed remarkable growth in recent years.

Artificial Intelligence (AI) helps the vehicle to take decision in complex environment. AI is utilized in automobiles industry for smart mobility. At present, automotive industry has employed advanced driver assistance system (ADAS) and with increase amount of embedded intelligent the industry is progressing towards semi-autonomous vehicle. AI enables real-time recognition of surroundings and automates the vehicle mobility, controls in-vehicle systems, and eventually prevents accident. The various applications of AI in automobile sector is road tracking, capturing driver's gesture and expression, passenger experience, fleet management, weather monitoring, predictive maintenance, location search, E-payment and in-vehicle system control.

Artificial Intelligence (AI) helps the vehicle to take decision in complex environment. AI is utilized in automobiles industry for smart mobility. At present, automotive industry has employed advanced driver assistance system (ADAS) and with increase amount of embedded intelligent the industry is progressing towards semi-autonomous vehicle. AI enables real-time recognition of surroundings and automates the vehicle mobility, controls in-vehicle systems, and eventually prevents accident. The various applications of AI in automobile sector is road tracking, capturing driver's gesture and expression, passenger experience, fleet management, weather monitoring, predictive maintenance, location search, E-payment and in-vehicle system control.

The increasing availability and accuracy of AI has stimulated uses of AI technologies in mainstream user-facing applications and services. Along with opportunities for infusing valuable AI services in a wide range of products come challenges and questions about best practices and guidelines for human-centered design. A dedicated team of Microsoft researchers addressed this need by synthesizing and validating a set of guidelines for human-AI interaction. This work marks an important step toward much-needed best practices for the complexities AI designers face. The integration of AI services such as prediction, recognition, and natural language understanding brings multiple new considerations to the fore for designers.

A Utah driver turned on the semi-autonomous functions of her Tesla vehicle and then didn't touch the steering wheel again for 80 seconds before slamming into a firetruck stopped at a red light last week, a summary of data from the car released Wednesday showed. The National Highway Traffic Safety Administration has sent its special crash investigations team to the state, the agency said as details about the Friday evening crash became public Wednesday. According to South Jordan police's summary of technician findings, the 28-year-old driver had repeatedly enabled and disabled the Autopilot features of her Tesla Model S throughout the course of her drive. She took her hands off the wheel more than a dozen times, twice for more than a minute each. The driver re-enabled Autopilot 1 minute and 22 seconds before the crash, let go of the wheel 2 seconds later and then didn't touch the wheel again before hitting the truck at 60 mph (97 kph).

It's found just the right place in Arizona, which has continued its lax regulatory policy for autonomous vehicles after an Uber self-driving Volvo killed a pedestrian on March 18 in Tempe. Nuro's co-founder and president, David Ferguson, signed a registration letter to the state Department of Transportation for the company on April 17, confirming that it planned to start testing fully autonomous vehicles on Arizona roads. On March 1, Governor Doug Ducey published an executive order to address fully autonomous vehicles, adding a modicum of oversight to his pro-business policy. All companies that intend to put fully driverless vehicles on Arizona roads in the near future, or are already testing them on roads, were ordered to register with the state within 60 days. As of May 2, Nuro was one of only two companies that had filed the required statement.

Former Indy car racer Sam Schmidt has a million-dollar car that allows him to do something that people said he would never be able to do again – drive on his own. But he still can't wait for fully autonomous vehicles to arrive. Not for driving on the track, where he feels fully safe manoeuvring his modified 2016 Corvette Stingray by using special gears created for quadriplegics. Rather, Schmidt says he needs the safety features found in autonomous cars to face the intimidating streets of Las Vegas, where he lives. "I don't feel comfortable on the street," says Schmidt, who lost the use of his four limbs in a 2000 crash on a racetrack in Orlando.

For this self-driving car, the roadside hazards included traffic jams, undisciplined bystanders--and centuries-old cannons. That's what you get when you demonstrate your latest technology at the National Army Museum in central Paris, as French companies Safran and Valeo did on Friday. Safran, a defense contractor, and Valeo, an automotive parts manufacturer, kitted out a Volkswagen CC with radar, lidar and all-round cameras for their demonstration, and let it loose on a winding track around the museum grounds. They wanted to show how close the European automotive industry is to its goal of having self-driving cars for sale in 2020. There were no wheel-spins or clouds of dust: This was a simulated urban environment with traffic lights, slow-moving or stopped vehicles ahead, and speed limits of 20 km/h or less.