Baidu's autonomous vehicle platform, Apollo, gets an upgrade. The Chinese IT firm, which started out ... [ ] as a Google imitator, is now going toe-to-toe with Google subsidiary Waymo, as well as Samsung and Intel. Baidu wants to one up Google on its AI powered car platform called Apollo. They might pull just pull it off. In any event, they at least have to be considered in the same league.
Mischief can happen when AI is let loose in the world, just like any technology. The examples of AI gone wrong are numerous, the most vivid in recent memory being the disastrously bad performance of Amazon's facial recognition technology, Rekognition, which had a propensity to erroneously match members of some ethnic groups with criminal mugshots to a disproportionate extent. Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy? "This is a really good question, and one we are actively working on, "Sergey Levine, assistant professor with the University of California at Berkeley's department of electrical engineering and computer science, told ZDNet by email this week. Levine and colleagues have been working on an approach to machine learning where the decisions of a software program are subjected to a critique by another algorithm within the same program that acts adversarially.
For a few years now, research and development in driverless cars have evolved tremendously, and several intuitive and creative changes have been witnessed. There have been various demonstrations of these autonomous vehicles by the auto giants both on and off roads. However, the measure of safety has always been a serious concern in self-driving cars. There have been severe accidents during the trials on-road of various driverless cars. For instance, in 2016, a Tesla driver died in a fatal crash while using autopilot mode.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Canadian man has been charged after he was found asleep at the wheel of a self-driving Tesla traveling over 93 mph down a highway in the province of Alberta, authorities said on Thursday. The July 9 incident occurred after authorities received a complaint that a Model S Tesla was speeding on a highway near the town of Ponoka, located about 60 miles south of Edmonton, according to a release by Alberta Royal Canadian Mounted police (RCMP). "The car appeared to be self-driving, traveling over 140km/h, with both front seats completely reclined and both occupants appearing to be asleep," the RCMP said in a statement.
Any good driver who is about to change lanes knows it's important to glance over their shoulder to ensure there are no vehicles in their blind spot -- and such real-time awareness of nearby vehicles is no less critical for autonomous driving systems. That's why self-driving technologies rely on a robust perception backbone that is expected to identify all relevant agents in the environment, including accurate "pose and shape" estimation of other vehicles sharing the road. Autonomous vehicle systems have evolved their own digital approaches to shoulder-checking, leveraging data from one of their most common sensing modalities, LiDAR. Now, a team of researchers from Pittsburgh-based autonomous vehicle technology company Argo AI, Microsoft, and CMU have introduced a novel network architecture for jointly estimating the shape and pose of vehicles even from partial LiDAR observations. Existing SOTA methods for pose and shape prediction typically first estimate the pose of an unaligned partial point cloud then apply that pose to the partial input before estimating the shape. However, this encoder-pose decoder and encoder-shape decoder architecture can result in shape estimation suffering from any errors in the pose estimation network's output and, eventually, poor completion performance.
Taking a snooze at your car's steering wheel as it hurdles down a freeway is a terrible idea, but with the continued expansion of self-driving car technology, getting a few extra minutes of sleep on your commute may not remain a pipe dream for much longer. But there are more than a few issues with this vision of the future. Chief among them is that the people don't trust self-driving cars, at least not enough to confidently put their lives in vehicles' "hands." Research from computer scientists in Germany could swing public opinion, however. Armed with a new algorithm, autonomous vehicles would be able to make safety calls in real-time.
An algorithm makes self-driving cars'accident-proof' as long as other human drivers on the road act responsibly, scientists claim. German researchers developed the algorithm with data collected from vehicles in the real-world and tested it in computer simulations. Assuming that other traffic drivers follow the rules of the road, the algorithm can take into account unexpected events, such as the appearance of cyclists. Autonomous vehicles can only be widely adopted once they can be trusted to drive more safely than human drivers. Therefore, teaching them how to respond to unique situations to the same capability as a human will be crucial to their full roll out.
Carrying out regular'pen-and-paper' exercises can reduce nausea during travel by more than 50 per cent, UK scientists claim. Cognitive training tasks, including identifying how patterns would appear on transparent paper when folded, help'train the brain' to reduce feelings of nausea in-transit, they say. Motion sickness, which creates a sensation of wooziness, can occur during car travel, at sea or even while using a virtual reality headset. But it's also an issue for passengers in self-driving cars, who are free to read, watch films and play video games thanks to the autonomous technology. Engaging in tasks before a journey was found to be effective at reducing motion sickness for passengers in both a driving simulator and on-the-road experiments, the experts found.
Currently, society faces numerous transportation inefficiencies. In addition to this, the average motor vehicle accident can cost up to over a million dollars based on the severity. Through subsequent increased insurance and potential legal fees, consumers unmistakably bare a significant burden. Lastly, people on average spend several thousand dollars on fuel annually. Clearly, there must be a solution to optimize mobility for everyone.
Recent advances in computer vision have revolutionized many areas of research including robotics, automation, and self-driving vehicles. The self-driving car industry has grown markedly in recent years, in no small part enabled by use of state-of-the-art computer vision techniques. However, there remain many challenges in the field. One of the most difficult problems in autonomous driving is perception. Once autonomous vehicles have an accurate perception of the world around them, planning and control become easier.