Rapid developments in technology require professionals to upgrade their skills for technology-centered jobs of tomorrow. Srikanth Vidapanakal, who has been into data for more than 18 years, was inquisitive to learn about new technologies. He did a Self-Driving Car Engineer Nanodegree that helped him acquire advanced skills and landed him with a job in automation sector. Srikanth is an example of lifelong learning where staying relevant in the age of rapidly changing technologies is the need of the hour. In 2017, research suggested that AI and robotics could collectively take over 800 million jobs worldwide by 2030.
Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the 'observer' vehicle from that of the external 'observed' vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.
Connected car startups raked in over $1 billion in funding last year, even though most consumers have yet to hear the term, connected car. With more than 1,700 autotech startups now in the game, competition is ramping up at all levels of the automotive IoT ecosystem. From automotive cyber security solutions providers to connected head-up display companies, new autotechs are coming online almost daily. Growth and sustainability of the connected car market demands a symbiotic relationship between automakers and 3rd-party tech providers.Without major automakers, there is no market for autotech products. With 250 million connected vehicles projected to be on the road by 2020, there's plenty of opportunity for automakers, and for the connected car startup.
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data and label augmentation using only one short range fisheye camera. We present our simulator and how it can be used as a consistent metric for lateral end-to-end control evaluation. Experiments are conducted on a custom dataset corresponding to more than 10000 km and 200 hours of open road driving. Finally we evaluate this model on real world driving scenarios, open road and a custom test track with challenging obstacle avoidance and sharp turns. In our simulator based on real-world videos, the final model was capable of more than 99% autonomy on urban road
Recent algorithmic improvements and hardware breakthroughs resulted in a number of success stories in the field of AI impacting our daily lives. However, despite its ubiquity AI is only just starting to make advances in what may arguably have the largest impact thus far, the nascent field of autonomous driving. In this work we discuss this important topic and address one of crucial aspects of the emerging area, the problem of predicting future state of autonomous vehicle's surrounding necessary for safe and efficient operations. We introduce a deep learning-based approach that takes into account current state of traffic actors and produces rasterized representations of each actor's vicinity. The raster images are then used by deep convolutional models to infer future movement of actors while accounting for inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.
The other drivers wouldn't have noticed anything unusual as the two sleek limousines with German license plates joined the traffic on France's Autoroute 1. But what they were witnessing -- on that sunny, fall day in 1994 -- was something many of them would have dismissed as just plain crazy. It had taken a few phone calls from the German car lobby to get the French authorities to give the go-ahead. But here they were: two gray Mercedes 500 SELs, accelerating up to 130 kilometers per hour, changing lanes and reacting to other cars -- autonomously, with an onboard computer system controlling the steering wheel, the gas pedal and the brakes. Decades before Google, Tesla and Uber got into the self-driving car business, a team of German engineers led by a scientist named Ernst Dickmanns had developed a car that could navigate French commuter traffic on its own. The story of Dickmann's invention, and how it came to be all but forgotten, is a neat illustration how technology sometimes progresses: not in small steady steps, but in booms and busts, in unlikely advances and inevitable retreats --"one step forward and three steps back," as one AI researcher put it. It's also a warning of sorts, about the expectations we place on artificial intelligence and the limits of some of the data-driven approaches being used today.
Elon Musk and many of the world's most respected artificial intelligence researchers have committed not to build autonomous killer robots. The public pledge not to make any "lethal autonomous weapons" comes amid increasing concern about how machine learning and AI will be used on the battlefields of the future. The signatories to the new pledge – which includes the founders of DeepMind, a founder of Skype, and leading academics from across the industry – promise that they will not allow the technology they create to be used to help create killing machines. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.
Listen to your vehicle - this is an advice that all car and motorcycle owners are given when they're getting to know more about the vehicle. Now, a new AI service developed by 3Dsignals, an Israel based start-up is doing just that. The AI system can detect an impending failure in cars or other machines, just by listening to the sound. The system depends on deep learning technique to identify the noise patterns of a car. As per a report by IEEE spectrum, 3Dsignals promises to reduce machinery downtime by 40% and improve efficiency.
Americans spend 8 billion hours stuck in traffic every year. Deep neural networks can help! DeepTraffic is a deep reinforcement learning competition. The goal is to create a neural network to drive a vehicle (or multiple vehicles) as fast as possible through dense highway traffic. What you see above is all you need to succeed in this competition.