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Perfecting mapping with AI and machine learning - Tech Wire Asia

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Across the world, mapping technology with Artificial Intelligence (AI) and machine learning allow users to have a variety of choices on their travels. Be it driving, flying, or walking, GPS systems are now a lifesaver in keeping users on track. Before this, most of us often used old maps or would buy travel maps whenever we wanted to move around. Today, map applications are not only available on GPS devices, but also on our mobile phones and are even built into our vehicles to provide better route directions. Despite this, there are still some challenges when it comes to mapping and location tagging.



AADS: Augmented autonomous driving simulation using data-driven algorithms

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.


Operationalizing Machine Learning for the Automotive Future

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It's no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago. These complex use cases require some rigorous MLOps planning.


How BMW Powers its Processes with Artificial Intelligence

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In this decade, companies across the globe have embraced the potential of artificial intelligence for digital transformation and enhanced customer experience. One important application of AI is enabling companies to use the pools of data available with them for smart business use. BMW is one of the world's leading manufacturers of premium automobiles and mobility services. BMW uses artificial intelligence in critical areas like production, research and development, and customer service. BMW also runs a project dedicated to this technology called Project AI, for efficient use of artificial intelligence.


TaaS Magazine: AUTONOMOUS Cars and Data Management - Bridgeworks

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There are many predictions about connected and autonomous vehicles, some of them suggesting that fully autonomous, levels 4 and 5 vehicles will begin to become commonplace on public roads from 2025. A study by Vynz Research says the global connected and autonomous vehicle market size was 17.7 million units in 2019; and it predicts that this will reach 51.2 million units by 2025 – a compound growth rate of 17.1% during the period of 2020 to 2025.At present, most vehicles aren't fully autonomous, yet still increasingly rely upon data to operate. With their emergence will be a growth in data. Rich Miller writes in his article for Data Center Frontier, 'Rolling Zettabytes: Quantifying the Data Impact of Connected Cars': "The Automotive Edge Computing Consortium (AECC) is working to help stakeholders understand the infrastructure requirements for connected cars. At Edge Computing World, AECC board member, Vish Nandlall, outlined the group's findings on the volume of data created by autonomous cars and the challenges they will create."


Scaling Up Chatbots for Corporate Service Delivery Systems

Communications of the ACM

Conversational agents, or chatbots, providing question-answer assistance on smart devices, have proliferated in recent years and are poised to transform online customer services of corporate sectors.1,6 Implemented through dialogue management systems, chatbots converse through voice-based and textual dialogue, and harness natural language processing and artificial intelligence to recognize requests, provide responses, and predict user behavior.5,28 Market analysts concur on current adoption trends and the magnitude of growth and impact of chatbots anticipated in the next five years. According to a report by Grand View Research, for instance, already 45% of users prefer chatbots as the primary point of communications for customer service enquiries, translating into a global'chatbot' market of $1.23 billion by 2025, at a compounded annual growth rate (CAGR) of 24.3%.9 The strategy for conducting conversations using chatbots requires an efficient resolution of two key aspects. First, user queries or automatically perceived needs through user interactions have to be interpreted and mapped into categories, or user intents. This is based on historical processing of queries and needs, and the use of intent classification techniques.12 Second, conversations must be constructed for specific intents using frame-based dialogue management2 and neural response generation techniques.15 In frame-based dialogue management, the chatbot needs to converse with the user to have a fully filled frame (for example, flight information) in which all slot values are provided by the user (for example, airline carrier, departure time, departure location, and arrival location). The dialogue flow is constructed through an ordered sequence of frames.


Pinaki Laskar on LinkedIn: #MLOps #AI #machinelearning

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #MLOps is the key for productionized ML system? ML model code is only a small part ( 5–10%) of a successful ML system, and the objective should be to create value by placing ML models into production. F1 score) while stakeholders focus on business metrics (e.g. Improving labelling consistency is an iterative process, so consider repeating the process until disagreements are resolved as far as possible. For instance, partial automation with a human in the loop can be an ideal design for AI-based interpretation of medical scans, with human judgement coming in for cases where prediction confidence is low.


When Smart Cars make Bad Choices

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Its 2030 and a SUV driven by an Autonomous Driving System (ADS) is heading west on a highway. The SUV contains two parents in the front seats and two small children in the back seat. The SUV is going the speed limit of 100 km/hour. The SUV drives through a tight corner and as the SUV makes the final turn a large bull moose weighing over six hundred kilograms shambles onto the road. The autonomous driving system driving the SUV was trained to select the best alternative out of as set of possible outcomes and so the SUV abruptly swerves into the left lane currently occupied by a small sedan going the same speed as the SUV. The SUV ADS had determined that saving the lives of two adults and two children was the greater good even though there was a significant risk that the small sedan would be forced into oncoming traffic travelling East putting the two adult occupants at mortal risk.


When self-driving cars are coming, for real

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Self-driving features have been creeping into automobiles for years, and Tesla (TSLA) even calls its autonomous system "full self-driving." That's hype, not reality: There's still no car on the market that can drive itself under all conditions with no human input. But researchers are getting close, and automotive supplier Mobileye just announced it's deploying a fleet of self-driving prototypes in New York City, to test its technology against hostile drivers, unrepentant jaywalkers, double parkers, omnipresent construction and horse-drawn carriages. The company, a division of Intel (INTC), describes NYC as "one of the world's most challenging driving environments" and says the data from the trial will push full self-driving capability closer to prime time. In an interview, Mobileye CEO Amnon Shashua said fully autonomous cars could be in showrooms by the end of President Biden's first term.