question: How self-driving cars are able to stop at intersections with no traffic lights and still know when it’s alright/safe to turn.
comment: > We use all our senses to make a decision like that; like sight, hearing and judging of the speed and acceleration of the other car. The sensors and computer of a self-driving car are better at that than we are. I'm not sure why you think they wouldn't be. e.g. lidar is much more precise than the eye at judging speeds.
passages: Section::::Methodology.
  
  In the figure, an example of the RCS methodology for designing a control system for autonomous onroad driving under everyday traffic conditions is summarized in six steps. 
   Section::::Nature of the digital technology.:Digital traces.
  
  Autonomous vehicles are equipped with different sorts of sensors and radars. As said, this allows them to connect and interoperate with computers from other autonomous vehicles and/or roadside units. This implies that autonomous vehicles leave digital traces when they connect or interoperate. The data that comes from these digital traces can be used to develop new (to be determined) products or updates to enhance autonomous vehicles' driving ability or safety.
  
  Section::::Nature of the digital technology.:Modularity.
   One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.
   In May 2018, researchers from MIT announced that they had built an automated car that can navigate unmapped roads. Researchers at their Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system, called MapLite, which allows self-driving cars to drive on roads that they have never been on before, without using 3D maps. The system combines the GPS position of the vehicle, a "sparse topological map" such as OpenStreetMap, (i.e. having 2D features of the roads only), and a series of sensors that observe the road conditions.
  
  Heavy rainfall, hail, or snow could impede the car sensors.
   Self-driving cars are already exploring the difficulties of determining the intentions of pedestrians, bicyclists, and animals, and models of behavior must be programmed into driving algorithms. Human road users also have the challenge of determining the intentions of autonomous vehicles, where there is no driver with which to make eye contact or exchange hand signals. Drive.ai is testing a solution to this problem that involves LED signs mounted on the outside of the vehicle, announcing status such as "going now, don't cross" vs. "waiting for you to cross".
   Driverless vehicles require some form of machine vision for the purpose of visual object recognition. Automated cars are being developed with deep neural networks, a type of deep learning architecture with many computational stages, or levels, in which neurons are simulated from the environment that activate the network. The neural network depends on an extensive amount of data extracted from real-life driving scenarios, enabling the neural network to "learn" how to execute the best course of action.
   There have so far been no complete implementation of peer-to-peer networking on the scale required for traffic: each individual vehicle would have to connect with potentially hundreds of different vehicles that could be going in and out of range.
  
  In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for automated cars. The intersections will have no traffic lights and no stop signs, instead using computer programs that will communicate directly with each car on the road.
   In May 2018, Drive.ai released a video featuring an orange Nissan NV200 driverless vehicle navigating Frisco streets. The video featured private streets as well as busy intersections and a traffic circle. It also featured a display showing the car's sensors and cameras dealing with objects on the road and highlighted operation through low-angle sunlight that would obscure typical sensors. The car's object recognition systems identify and route around other vehicles, pedestrians and cyclists safely. Drive.ai's cars include screens on the outside of the car, one on the hood, one on the back, and one on each side, to communicate with pedestrians. The screens feature prompts such as "Passengers Entering/Exiting' or "Waiting for You to Cross."
   Also part of the field of autonomous vehicles and automated driving is the Live Roads technology. Here is currently developing such a technology that will be able to alert drivers of conditions such as weather to alert other drivers of possible hazards, or to avoid a particular area whilst driving. An example is the aggregation of data from windshield wipers and slipping tyres to notify other drivers to avoid an ice-filled area. The company is also investigating Humanised Driving where data is collected on driving habits on roads, and provisioning this data to allow automated cars to follow how drivers behave (speed, traffic lights etc.) when driving on certain roads.
   In constrained outdoor areas, some robots, such as the John Deere Gator, simply surround the perimeter with radio beacons and use simple triangulation from three or more beacons to localize and navigate. Beacons are also used indoors by older AGVs in factories.
  
  Section::::Programming.
  
  Much research software for autonomous robots is Free Software or Open Source Software, including: Robot Operating System, Carmen from Carnegie Mellon, Player/Stage/Gazebo from the University of Southern California and the ARIA APIs from MobileRobots Inc. URBI with a Free Software SDK, is used in many universities.
   Section::::Fields of application.
  
  Section::::Fields of application.:Transport systems.
   Section::::Wireless communications.:Sensing technologies.:Inductive loop detection.
  
  Inductive loops can be placed in a roadbed to detect vehicles as they pass through the loop's magnetic field. The simplest detectors simply count the number of vehicles during a unit of time (typically 60 seconds in the United States) that pass over the loop, while more sophisticated sensors estimate the speed, length, and class of vehicles and the distance between them. Loops can be placed in a single lane or across multiple lanes, and they work with very slow or stopped vehicles as well as vehicles moving at high speed.
   Section::::Nature of the digital technology.
  
  Autonomous vehicles, as digital technology, have certain characteristics that distinguish them from other types of technologies and vehicles. Due to these characteristics, autonomous vehicles are able to be more transformative and agile to possible changes. The characteristics will be explained based on the following subjects: homogenization and decoupling, connectivity, reprogrammable and smart, digital traces and modularity.
  
  Section::::Nature of the digital technology.:Homogenization and decoupling.
   Vehicles traveling in the vicinity of an accident scenario, for example, are simultaneously queried by the in-vehicle police intelligent beacon system computer which repeatedly updates and processes all dynamic passing vehicle data received, identifying and classifying all passing vehicles in real-time – for example, an aural visual command instruction is sent to all the in-vehicle emergency warning beacon system computers as a reminder that no rubbernecking, for example, or viewing of the accident is permitted and vehicles are instructed to safely maintain a given speed limit. Ease of managing, operating, and reducing traffic congestion of the transportation system is therefore achieved.
   Based on the RCS Reference Model Architecture the NIST has developed a Real-time Control System Software Library. This is an archive of free C++, Java and Ada code, scripts, tools, makefiles, and documentation developed to aid programmers of software to be used in real-time control systems, especially those using the Reference Model Architecture for Intelligent Systems Design.
  
  Section::::Applications.
  
  BULLET::::- The ISAM Framework is an RCS application to the Manufacturing Domain.
  
  BULLET::::- The 4D-RCS Reference Model Architecture is the RCS application to the Vehicle Domain, and
   The data from the different sensing technologies can be combined in intelligent ways to determine the traffic state accurately. A Data fusion based approach that utilizes the road side collected acoustic, image and sensor data has been shown to combine the advantages of the different individual methods.
  
  Section::::Intelligent transportation applications.
  
  Section::::Intelligent transportation applications.:Emergency vehicle notification systems.
   The main stumbling block to the widespread introduction of such systems is the fact that most vehicles on the road are unable to communicate with the computer systems that town and city authorities use to control traffic lights. However, the trial in Harris County, Texas, referred to above, uses a simple system based on signals received from drivers' cell phones and it has found that even if only a few drivers have their phone switched on, the system is still able to produce reliable data on traffic density. 
   BULLET::::- Autonomous mobile robots: Google's self-driving cars are cloud robots. The cars use the network to access Google's enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions. Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars.
   In 2004, a group of Linköping University students working with Volvo developed a project Evolve. The Evolve car can automatically perform parallel parking by using sensors and a computer to control steering, acceleration and braking of Volvo S60.
   The lane keeping assist system is being achieved in modern driverless vehicle systems using image processing techniques called hough transform and canny edge detection techniques. These advanced image processing techniques derive lane data from forward facing cameras attached to the front of the vehicle. Real-time image processing using powerful computers like Nvidia's Drive PX1 are being used by many Vehicle OEMs to achieve fully autonomous vehicles in which Lane detection algorithm plays a key part. Advanced lane detection algorithms are also being developed using deep learning and neural network techniques. Nvidia has achieved high accuracy in developing self-driving features including lane keeping using the neural network based training mechanism in which they use a front facing camera in a car and run it through a route and then uses the steering input and camera images of the road fed into the neural network and make it 'learn'. The neural network then will be able to change the steering angle based on the lane change on the road and keep the car in the middle of the lane.
   The agent has the same two (left and right) symmetric sensors (e.g. light detectors), but each one stimulating a wheel on the other side of the body. It obeys the following rule:
  
  BULLET::::- More light left → right wheel turns faster → turns towards the left, closer to the light.
  
  As a result, the robot follows the light; it moves to be closer to the light.
  
  Section::::Behavior.
  
  In a complex environment with several sources of stimulus, Braitenberg vehicles will exhibit complex and dynamic behavior.
   BULLET::::- "Driven", series 4 episode 11 of the 2003 TV series "NCIS" features a robotic vehicle named "Otto", part of a high-level project of the Department of Defense, which causes the death of a Navy Lieutenant, and then later almost kills Abby.
  
  BULLET::::- The TV series "Viper" features a silver/grey armored assault vehicle, called "The Defender", which masquerades as a flame-red 1992 Dodge Viper RT/10 and later as a 1998 cobalt blue Dodge Viper GTS. The vehicle's sophisticated computer systems allow it to be controlled via remote on some occasions.
   Section::::C-Pilot ADAS project.
  
  In August 2016 Cognitive Technologies started its own ADAS development project C-Pilot for ground transport control automation.
  
  The project has three stages:
  
  BULLET::::- By 2017, the company will develop a number of features for warning the driver in cases of road situations requiring increased attention: lane departure warning system, traffic sign recognition system, forward collision warning system, blind spot detection system, pedestrian protection system
   Section::::Nature of the digital technology.:Reprogrammable.
   Typically, PCAM systems are part of the technology in self-driving cars such as the Tesla Autopilot and use an integrated forward-facing camera and radar or lidar system designed to help mitigate or avoid a frontal crash. However, PCAM technologies do not require self-driving technologies, just cameras and radar. Sometimes, these can be enhanced with the addition of low-light detection for pedestrians and bicycles. The Nissan Leaf provides some PCAM capabilities in a commercial vehicle, but most manufacturers do not. For the 2019 model year, two Subaru models the Outback wagon and related Legacy sedan will join the redesigned Forester and Ascent SUVs to include the automaker's EyeSight crash-avoidance system as standard equipment. Subaru was one of the earliest adopters of a PCAM system (EyeSight) which is in phase III. Key players operating in the global PCAM market as of 2016 include Audi AG, Volvo Car Corporation, Continental AG, Robert Bosch GmbH, Toyota Motor Corporation, ZF Friedrichshafen AG, Autoliv Inc., Mobileye N.V., Subaru Corporation, Valeo, Honda Motor Co., Ltd., General Motors, Ford Motor Company, Daimler AG, Denso Corporation, Nissan Motor Corporation, BMW AG, and Magneti Marelli SpA. The PCAM market in North America is projected to reach US$1,997.3 million by 2025. In 2016, the U.S. Department of Transportation's National Highway Traffic Safety Administration officially announced that automakers in the U.S. have to include the autonomous emergency braking system as a standard feature for all cars and trucks by 2022: this is a key component of PCAM. A detailed explanation for manufacturers offering emergency braking as part of a pre-collision system and often PCAM is provided in .
  
presuppositions: Self driving cars can't know when to turn at an intersection with no traffic lights. Self driving car sensors do not work as well as human senses.
corrections: Self driving cars have sensors that can detect other cars. Self driving cars have sensors that work better than human senses.
raw_presuppositions: Self driving cars can't know when to turn at an intersection with no traffic lights. Self driving car sensors do not work as well as human senses. Self driving cars can't know when to turn at an intersection with no traffic lights.
raw_corrections: Self driving cars have sensors that can detect other cars. Self driving cars have sensors that work better than human senses. Self driving cars have sensors that can detect other cars.