Imagine you are in the year 2030, combustion vehicles have almost started losing charm to electric vehicles and hybrid vehicles. But there is another clause why electric vehicles and hybrid vehicles are into big swaps, ‘Autonomous Driving’ or we can also call it ‘Self Driving Car’! All this dates back to experiments conducted during the 1920s in the United States of America. And in fact, it’s still common to see vehicles in a trial in progress on United States of America roads. Seeing the technology bouncing, companies like Amazon, Google, Apple, and Tesla have come up with a huge investment of over $ 8 billion to bring out to the world.
Also with popular technologies such as machine learning, artificial intelligence, computer vision, and data analytics autonomous driving cars could be much earlier launched as predicted. This could be a massive breakthrough for applications such as regular taxis, campus shuttles, the elderly and disabled, insurance companies, and children. Imagine all you need to do is just reach out to the vehicle and select the final destination. Take a short nap fearlessly until your venue arrives! Shuttling students/passengers from the main gate to the main campus building and back. The same can be possible for highway ferries from one end to another end. All was possible for deeper research in the fields of state estimation and localization, visual perception, motion planning, and last but not least is the sensor fusion and tracking tools. Researchers and engineers across the globe are yielding much more data that could be driven out of these technologies and which in the end can refine the self-driving experience.
If you are a regular follower of this technology in the lap, media reporting incidents of crashes or mismanagement is quite common. In mid‑October 2015, Tesla Motors rolled out version 7 of their software in the US that included Tesla Autopilot capability. On 9 January 2016, Tesla rolled out version 7.1 as an over-the-air update, adding a new "summon" feature that allows cars to self-park at parking locations without the driver in the car. Tesla's autonomous driving features are currently classified as a Level 2 driver assistance system according to the Society of Automotive Engineers (SAE) five levels of vehicle automation. At this level, the car can be automated but requires the full attention of the driver, who must be prepared to take control at a moment's notice. Autopilot should be used only on limited-access highways, and sometimes it will fail to detect lane markings and disengage itself. In urban driving, the system will not read traffic signals or obey stop signs. The system also does not detect pedestrians or cyclists.
Tesla Model S Autopilot system in use in July 2016; it was only suitable for limited-access highways, not for urban driving. Among other limitations, it could not detect pedestrians or cyclists.
On 20 January 2016, the first known fatal crash of a Tesla with Autopilot occurred in China's Hubei province. According to China's 163.com news channel, this marked "China's first accidental death due to Tesla's automatic driving (system)". Initially, Tesla pointed out that the vehicle was so badly damaged from the impact that their recorder was not able to conclusively prove that the car had been on Autopilot at the time; however, 163.com pointed out that other factors, such as the car's absolute failure to take any evasive actions prior to the high-speed crash, and the driver's otherwise good driving record, seemed to indicate a strong likelihood that the car was on Autopilot at the time. A similar fatal crash occurred four months later in Florida. In 2018, in a subsequent civil suit between the father of the driver killed and Tesla, Tesla did not deny that the car had been on Autopilot at the time of the accident and sent evidence to the victim's father documenting that fact.
According to Tesla, starting 19 October 2016, all Tesla cars are built with hardware to allow full self-driving capability at the highest safety level (SAE Level 5). The hardware includes eight surround cameras and twelve ultrasonic sensors, in addition to the forward-facing radar with enhanced processing capabilities. The system will operate in "shadow mode" (processing without taking action) and send data back to Tesla to improve its abilities until the software is ready for deployment via over-the-air upgrades. After the required testing, Tesla hopes to enable full self-driving by the end of 2020 under certain conditions. Vehicle networking may be desirable due to difficulty with computer vision being able to recognize brake lights, turn signals, buses, and similar things. However, the usefulness of such systems would be diminished by the fact current cars are not equipped with them; they may also pose privacy concerns.
Individual vehicles may benefit from information obtained from other vehicles in the vicinity, especially information relating to traffic congestion and safety hazards. Vehicular communication systems use vehicles and roadside units as the communicating nodes in a peer-to-peer network, providing each other with information. As a cooperative approach, vehicular communication systems can allow all cooperating vehicles to be more effective.
According to a 2010 study by the US National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79 % of all traffic accidents. There has 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 of using computer programs that will communicate directly with each car on the road.
In 2017, Researchers from Arizona State University developed a 1/10 scale intersection and proposed an intersection management technique called Crossroads. It was shown that Crossroads is very resilient to network delay of both V2I communication and the Worst-case Execution time of the intersection manager. In 2018, a robust approach was introduced which is resilient to both model mismatch and external disturbances such as wind and bumps. Among connected cars, an unconnected one is the weakest link and will be increasingly banned from busy high-speed roads, as predicted by the Helsinki think tank, Nordic Communications Corporation, in January 2016.
Apart from this, talking about Indian roads and infrastructure for autonomous driving, the road map says the timeline could be a bit far because of missing standard roads and signals across the streets. Major government reforms and enforcing laws for the roads can accelerate things to get this technology on track. Most of the roads contain nonstandard speed breakers, uneven road patterns, bumps holes, cattle on road, missing signs, or less interactive roads which is absurd for self-driving vehicles. Indian roads are also unpredictable to the algorithm and hence would require different databases for making it through the road. The day all things are fixed together, autonomous driving could be implemented easily for trials first and then merge with mainstream traffic. It all depends on awareness among the people and how the government understands the urgency to launch and make the earth a better place to live.
So to conclude what is next? If everything goes well as planned, by 2030 or so, spotting and enjoying a driverless car ride is not far as the technology trend explains
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