How Smart Are Cars With Auto-Pilot
Autopilot comes standard on every new Tesla. For owners who took delivery of their cars without Autopilot, there are two Autopilot packages available for purchase, depending on when your car was built: Enhanced Autopilot and Full Self-Driving Capability.
How Smart Are Cars With Auto-Pilot
A self-driving car, also known as an autonomous car, driver-less car, or robotic car (robo-car), is a car that is capable of traveling without human input. Self-driving cars use sensors to perceive their surroundings, such as optical and thermographic cameras, radar, lidar, ultrasound/sonar, GPS, odometry and inertial measurement units. Control systems interpret sensory information to create a three-dimensional model of the vehicle's surroundings. Based on the model, the car then identifies an appropriate navigation path and strategies for managing traffic controls (stop signs, etc.) and obstacles.
The US allocated US$650 million in 1991 for research on the National Automated Highway System, which demonstrated automated driving through a combination of automation embedded in the highway with automated technology in vehicles, and cooperative networking between the vehicles and with the highway infrastructure. The programme concluded with a successful demonstration in 1997 but without clear direction or funding to implement the system on a larger scale. Partly funded by the National Automated Highway System and DARPA, the Carnegie Mellon University Navlab drove 4,584 kilometres (2,848 mi) across America in 1995, 4,501 kilometres (2,797 mi) or 98% of it autonomously. Navlab's record achievement stood unmatched for two decades until 2015, when Delphi improved it by piloting an Audi, augmented with Delphi technology, over 5,472 kilometres (3,400 mi) through 15 states while remaining in self-driving mode 99% of the time. In 2015, the US states of Nevada, Florida, California, Virginia, and Michigan, together with Washington, DC, allowed the testing of automated cars on public roads.
In November 2017, Waymo announced that it had begun testing driver-less cars without a safety driver in the driver position; however, there was still an employee in the car. An October 2017 report by the Brookings Institution found that $80 billion had been reported as invested in all facets of self driving technology up to that point, but that it was "reasonable to presume that total global investment in autonomous vehicle technology is significantly more than this".
Several classifications have been proposed to deal with the broad range of technological discussions pertaining to self-driving cars. One such proposal is to classify based on the following categories; car navigation, path planning, environment perception and car control. In the 2020s, it became apparent that these technologies are far more complex than initially thought. Even video games have been used as a platform to test autonomous vehicles.
SensingTo reliably and safely operate an autonomous vehicle, usually a mixture of sensors is utilized.Typical sensors include lidar (Light Detection and Ranging), stereo vision, GPS and IMU.Modern self-driving cars generally use Bayesian simultaneous localization and mapping (SLAM) algorithms,which fuse data from multiple sensors and an off-line map into current location estimates and map updates. Waymo has developed a variant of SLAM with detection and tracking of other moving objects (DATMO), which also handles obstacles such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) technologies to aid localization.
MapsSelf-driving cars require a new class of high-definition maps (HD maps) that represent the world at up to two orders of magnitude more detail. In May 2018, researchers from the Massachusetts Institute of Technology (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.
Path planningPath planning is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. Self-driving cars rely on path planning technology in order to follow the rules of traffic and prevent accidents from occurring. The large scale path of the vehicle can be determined by using a voronoi diagram, an occupancy grid mapping, or with a driving corridors algorithm. A driving corridors algorithm allows the vehicle to locate and drive within open free space that is bounded by lanes or barriers. While these algorithms work in a simple situation, path planning has not been proven to be effective in a complex scenario. Two techniques used for path planning are graph-based search and variational-based optimization techniques. Graph-based techniques can make harder decisions such as how to pass another vehicle/obstacle. Variational-based optimization techniques require a higher level of planning in setting restrictions on the vehicle's driving corridor to prevent collisions.
EmploymentCompanies working on the technology have an increasing recruitment problem in that the available talent pool has not grown with demand. As such, education and training by third-party organizations such as providers of online courses and self-taught community-driven projects such as DIY Robocars and Formula Pi have quickly grown in popularity, while university level extra-curricular programmed such as Formula Student Driver-less have bolstered graduate experience. Industry is steadily increasing freely available information sources, such as code, datasets and glossaries to widen the recruitment pool.
National securityIn the 2020s, from the importance of the automotive sector to the nation, self-driving car has become a topic of national security. The concerns regarding cybersecurity and data protection are not only important for user protection, but also in the context of national security. The trove of data collected by self-driving cars, paired with cybersecurity vulnerabilities, creates an appealing target for intelligence collection. Self-driving cars are required to be considered in a new way when it comes to espionage risk.
Moving obstaclesSelf-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".
The second challenge is known as risk compensation: as a system is perceived to be safer, instead of benefiting entirely from all of the increased safety, people engage in riskier behavior and enjoy other benefits. Semi-automated cars have been shown to suffer from this problem, for example with users of Tesla Autopilot ignoring the road and using electronic devices or other activities against the advice of the company that the car is not capable of being completely autonomous. In the near future, pedestrians and bicyclists may travel in the street in a riskier fashion if they believe self-driving cars are capable of avoiding them.
TrustIn order for people to buy self-driving cars and vote for the government to allow them on roads, the technology must be trusted as safe. Self-driving elevators were invented in 1900, but the high number of people refusing to use them slowed adoption for several decades until operator strikes increased demand and trust was built with advertising and features like the emergency stop button. There are three types of trust between human and automation. There is dispositional trust, the trust between the driver and the company's product; there is situational trust, or the trust from different scenarios; and there is learned trust where the trust is built between similar events.
Rationale for liabilityThere are different opinions on who should be held liable in case of a crash, especially with people being hurt. One study suggests requesting the owners of self-driving cars to sign end-user license agreements (EULAs), assigning to them accountability for any accidents. Other studies suggest introducing a tax or insurances that would protect owners and users of automated vehicles of claims made by victims of an accident. Other possible parties that can be held responsible in case of a technical failure include software engineers that programmed the code for the automated operation of the vehicles, and suppliers of components of the AV.
Implications from the Trolley ProblemA moral dilemma that a software engineer or car manufacturer might face in programming the operating software of a self-driving vehicle is captured in a variation of the traditional ethical thought experiment, the trolley problem: An AV is driving with passengers when suddenly a person appears in its way and the car has to commit between one of two options, either to run the person over or to avoid hitting the person by swerving into a wall, killing the passengers. Researchers have suggested, in particular, two ethical theories to be applicable to the behavior of automated vehicles in cases of emergency: deontology and utilitarianism. Deontological theory suggests that an automated car needs to follow strict written-out rules that it needs to follow in any situation. Utilitarianism, on the other hand, promotes maximizing the number of people surviving in a crash. Critics suggest that automated vehicles should adapt a mix of multiple theories to be able to respond morally right in the instance of a crash. Recently, some specific ethical frameworks i.e., utilitarianism, deontology, relativism, absolutism (monism), and pluralism, are investigated empirically with respect to the acceptance of self-driving cars in unavoidable accidents. 041b061a72