parking spot


AI Learns to Park - Deep Reinforcement Learning

#artificialintelligence

An AI learns to park a car in a parking lot in a 3D physics simulation. The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm, which is a Reinforcement Learning approach. Basically, the input of the Neural Network are the readings of eight depth sensors, the cars current speed and position, as well as its relative position to the target. The outputs of the Neural Network are interpreted as engine force, braking force and turning force.


Snagging Parking Spaces with Mask R-CNN and Python

#artificialintelligence

I live in a great city. But like in most cities, finding a parking space here is always frustrating. Spots get snapped up quickly and even if you have a dedicated parking space for yourself, it's hard for friends to drop by since they can't find a place to park. This might sound pretty complicated, but building a working version of this with deep learning is actually pretty quick and easy. All the tools are available -- it is just a matter of knowing where to find the tools and how to put them together.


Searching for a Parking Spot? AI Got It NVIDIA Blog

#artificialintelligence

Anyone who's circled a busy parking lot or city block knows that finding an open spot can be tricky. It all can turn a quick trip to the store into a high-stress ordeal. To park in these environments, autonomous vehicles need a visual perception system that can detect an open spot under a variety of conditions. Perceiving both indoor and outdoor spaces, separated by single, double or faded lane markings, as well as differentiating between occupied, unoccupied and partially obscured spots are key for such a system -- as is doing so under varying lighting conditions. Not every parking space is a perfect rectangle.


r/MachineLearning - [P] Agent Learns to Park a Car using Unity ML-Agents / Deep Reinforcement Learning (PPO)

#artificialintelligence

An AI learns to park a car in a parking lot in a 3D physics simulation. The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm. The input of the Neural Network are the readings of eight depth sensors, the cars current speed and position, as well as its relative position to the target. The outputs of the Neural Network are interpreted as engine force, braking force and turning force (continuous values).


Artificial Intelligence for Smart Cities

#artificialintelligence

According to the data published by the UN, the world population will reach up to a limit of 9.7 billion by the end of 2050. It is deduced that almost 70% of that population will be an urban population with many cities accommodating over 10m inhabitants. As the number grows, we'll have to encounter challenges regarding making a provision for resources and energy to all of the inhabitants and at the same time, avoiding environment deterioration. Another critical challenge is administration and management to prevent sanitation issues, mitigate traffic congestion, thwart crime, etc. But many of these problems can be tamed by the use of AI-enabled IoT.


How AI is transforming the Smart Cities IoT? [Tutorial] Packt Hub

#artificialintelligence

According to techopedia, a smart city is a city that utilizes information and communication technologies so that it enhances the quality and performance of urban services (such as energy and transportation) so that there's a reduction in resource consumption, wastage, and overall costs. In this article, we will look at components of a smart city and its AI-powered- IoT use cases, how AI helps with the adaption of IoT in Smart cities, and an example of AI-powered-IoT solution. Hence, a smart city would be a city that not only possesses ICT but also employs technology in a way that positively impacts the inhabitants. This article is an excerpt taken from the book'Hands-On Artificial Intelligence for IoT' written by Amita Kapoor. The book explores building smarter systems by combining artificial intelligence and the Internet of Things--two of the most talked about topics today.


Indian student creates algorithm that uses big data to find empty parking spots- Technology News, Firstpost

#artificialintelligence

An Indian student in the US has created a space-detecting algorithm that can help tackle the problem of finding a parking spot by using big data analytics and save a person's time and money. Sai Nikhil Reddy Mettupally, who is studying at The University of Alabama in Huntsville (UAH), has also won second prize at the 2018 Science and Technology Open House competition for his creation. According to a university press release, Sai's creation relies on big data analytics and deep-learning techniques to lead drivers directly to an empty parking spot. Big data analytics is a complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences. Sai conceived the idea shortly after the university transitioned to zone parking last fall.


Indian Student In US Uses Big Data Analytics To Tackle Parking Problem

#artificialintelligence

An Indian student in the US has created a space-detecting algorithm that can help tackle the problem of finding a parking spot by using big data analytics and save a person's time and money. Sai Nikhil Reddy Mettupally, who is studying at The University of Alabama in Huntsville (UAH), has also won second prize at the 2018 Science and Technology Open House competition for his creation. According to a university press release, Sai's creation relies on big data analytics and deep-learning techniques to lead drivers directly to an empty parking spot. Big data analytics is a complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences. Sai conceived the idea shortly after the university transitioned to zone parking last fall.


How is Transportation Being Transformed in Smart Cities

#artificialintelligence

Transportation has always been one of the most complicated problems that City Councils have to deal with. Until the rise of digital technologies, there have been two different approaches to traffic issues: building more and bigger highways (a good example is Los Angeles) or putting the focus on public transport, like most European cities. Both approaches are apparently very different but they share the same basis: investment in huge, expensive infrastructures. Smart cities -digital cities- approach traffic issues from a new, smarter perspective from the believe that we need neither more subways nor wider highways. New technologies based on Big Data, IoT, artificial intelligence and machine learning allow us to put new solutions on the table .


Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks

arXiv.org Machine Learning

Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks. We use neural networks to predict individual charging station usage statistics from the station's physical location within a network. We have shown this quickly gives accurate estimates of average usage statistics given a proposed configuration, without the need for running many computationally expensive simulations. The trained neural network can help EV charging network designers rapidly test various placements of charging stations under additional individual constraints in order to find an optimal configuration given their design objectives.