Artificial Intelligence: Empowering Futuristic Automotive Vehicles
Artificial Intelligence (AI) helps the vehicle to take decision in complex environment. AI is utilized in automobiles industry for smart mobility. At present, automotive industry has employed advanced driver assistance system (ADAS) and with increase amount of embedded intelligent the industry is progressing towards semi-autonomous vehicle. AI enables real-time recognition of surroundings and automates the vehicle mobility, controls in-vehicle systems, and eventually prevents accident. The various applications of AI in automobile sector is road tracking, capturing driver’s gesture and expression, passenger experience, fleet management, weather monitoring, predictive maintenance, location search, E-payment and in-vehicle system control.
Commercialization of self-driving vehicles is the biggest challenge which automotive industry is facing. Some big companies such as Google, Tesla and Uber have launched their test self-driving vehicles on road. In March 2018, a woman died in collision with Uber’s self-driving vehicle. The car’s LiDAR was unable to detect the woman in night as she was wearing black jacket. LiDAR detects object by emitting light and measuring reflected beams. As black is the least reflective color, it was unable to differentiate between surroundings. With increasing accidents people are not able to trust autonomous vehicles, so the automakers are slowly progressing from ADAS to semi-autonomous vehicle to make the full autonomous vehicle a reality. This accident could have been avoided if car was going slightly slow, then its software could have detected the woman. The advancement in speed adjustment in case or dark or snow may minimize the risk factor.
Creating and maintaining road map for self-driving vehicle is another challenge which automotive industry is facing. Building a 3D detailed map is time consuming process. The company first sends the manually drive vehicle all over the streets to get map using LiDAR camera. It creates 3D map for self-driving car with the help of LiDAR processing. Limited availability of 3D map is an obstacle in operation of self-driving car in most of cities.
Despite all these challenges AI in Automotive industry is set to pave the way for smart mobility where AI will be the driver, and passengers may not care about traffic congestion possibilities as the vehicle will have the ability to choose the best route, emergency control, fuel status and other important aspects.
Driverless car is the biggest driving factor of AI in automotive. Driverless car will not only drive themselves, they will also recognize the owner. Before driverless, connected cars are expected to penetrate market. Connected cars are equipped with internet access, which allow them to share internet with other connected devices outside as well as inside the cars. They can share information with their other connected devices, it can be another car or outside infrastructure because of availability of internet. In April 2018, Huawei and Group PSA showcased their first connected vehicle in Europe. Huawei and PSA has integrated connected car with IoT to improve mobility services. It includes natural voice recognition, navigation, vehicle status, journey history and driving style. In future we may witness some other features also such as personal assistant, vehicle diagnostic and maintenance functions.
According to Brookings Institute study more than $80 billion is invested in self-driving car from 2014 to 2018. The developers of AI in Automobiles industry will make the driverless car with zero human interference. AI would also help in building the cheaper car that can help in sensing outside weather and navigate through all the hindrances.
Since the consumer mobility pattern is changing, a major impact will be seen on Mobility-as-a-Service under car rentals and ride sharing. AI in Automotive will let people hire cars without any drivers through AI based autonomous vehicles. Also, ride sharing will be more feasible, as AI will monitor the fleet and schedule the vehicle after analyzing route and wait time.