Driving etiquette takes a different flavor for each locality as drivers not only comply with rules/laws but also abide by local unspoken convention. When to have the turn signal (indicator) on/off is one such etiquette which does not have a definitive right or wrong answer. Learning this behavior from the abundance of data generated from various sensor modalities integrated in the vehicle is a suitable candidate for deep learning. But what makes it a prime candidate for Federated Learning are privacy concerns and bandwidth limitations for any data aggregation. This paper presents a long short-term memory (LSTM) based Turn Signal Prediction (on or off) model using vehicle control area network (CAN) signal data. The model is trained using two approaches, one by centrally aggregating the data and the other in a federated manner. Centrally trained models and federated models are compared under similar hyperparameter settings. This research demonstrates the efficacy of federated learning, paving the way for in-vehicle learning of driving etiquette.

Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multi-agent environments, where agents need to anticipate other agents' intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases. In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. Leveraging the powerful Deep RL paradigm, we demonstrate that an iterative procedure of self-play can create progressively more diverse environments, leading to the learning of sophisticated and robust multi-agent policies. W e demonstrate this in a challenging multi-agent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. While the environment starts off simple, we increase its complexity by iteratively adding an increasingly diverse set of agents to the agent "zoo" as training progresses. Qualitatively, we find that through self-play, our policies automatically learn interesting behaviors such as defensive driving, overtaking, yielding, and the use of signal lights to communicate intentions to other agents. In addition, quantitatively, we show a dramatic improvement of the success rate of merging maneuvers from 63% to over 98%.

Hey buddy, pick a lane and stick with it! I was taken aback by a rude driver that had abruptly and without signaling opted to cut into my lane. I needed to quickly move into the lane next to me. Wait a second, unfortunately, none of the cars in that lane were willing to allow me into their jam packed lane. I'm sure that you likely have experienced the same kinds of frustrations in your daily commute as well. On the freeways here in Los Angeles, it seems like here aren't enough lanes for the number of cars. The lanes are often poorly marked and cars tend to radically veer toward each other. Plus, drivers illegally dart into and out of the HOV lane, and sometimes illegally use the emergency lane as a form of underhanded transit. We live in a world of lanes. On the open highways, there are usually a couple of lanes going in each direction.

Someday, perhaps, streets and highways will host only fully autonomous vehicles, wirelessly communicating and following algorithms that let them handle any situation they encounter. For now, though, city streets are filled with pedestrians, bicyclists, delivery trucks, double-parked cars, emergency vehicles, and construction crews, as well as human-operated cars with issues of their own. In this chaotic setting, self-driving cars face additional challenges beyond rapidly analyzing the complex environment and navigating through it. They also must keep their distracted occupants informed of issues potentially requiring attention. Equally important, they must continually coordinate their actions with humans, whether in other cars or on the street.

Abstract-- Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios. I. INTRODUCTION Autonomous driving has risen as one of the most impactful applications of Artificial Intelligence (AI), where it has the potential to change the way we live.

I love a good road trip. I've spent hundreds of thousands of miles in cars during my life, and the best times were when I knew it would be hours or even days before I reached my destination. Typically a friend (or friends) or family members would accompany me, but on a few occasions, it was just me, my music collection -- and scenery screaming past me at 70 miles per hour. In the past few years, more and more automakers have created semiautonomous systems so that you're no longer "alone" on these drives. One of the more robust (and most famous) is Tesla's Autopilot.

Nick Chater has put forward a bold claim in his recent book, The Mind Is Flat, as well as in an article and interview in Nautilus: that we don't have any unconscious thoughts. A metaphor that Chater, a behavioral scientist, dislikes is that of the iceberg, the tip of which is our consciousness, and the vast, submerged part is our unconscious. As Chater says in the Nautilus interview, this suggests that unconscious and conscious processes use the same kinds of representations, and that the kinds of things we are unconscious of we could be conscious of. He's certainly right that many brain processes go on that we're unaware of, and can't be aware of. Let's take visual recognition as an example.

Tonight, Tesla makes its cars autonomous. And it did it with an over-the-air update, effectively making tens of thousands of cars already sold to customers way better. There are two things to talk about here. There's the small story about the features and what the upgrade actually looks like and how it works. That's a good place to start: This is the biggest change to the visual display of the Model S and X ever.

Lane changing on highways is stressful. In this paper, we present RASCL, the Robotic Assistance System for Changing Lanes. RASCL combines state-of-the-art sensing and localization techniques with an accurate map describing road structure to detect and track other cars, determine whether or not a lane change to either side is safe, and communicate these safety statuses to the user using a variety of audio and visual interfaces. The user can interact with the system through specifying the size of their “comfort zone”, engaging the turn signal, or by simply driving across lane dividers. Additionally, RASCL provides speed change recommendations that are predicted to turn an unsafe lane change situation into a safe situation and enables communication with other vehicles by automatically controlling the turn signal when the driver attempts to change lanes without using the turn signal.