By VV Ravi Kumar & Parth M Doshi
Kumar is Deputy Director, Symbiosis Institute of Business Management, Pune, and Doshi is Deputy Manager, Deloitte India. Views are personal.
The Indian automotive sector, which until recently was struggling with several structural challenges including BS6 compliance, faces a double whammy amidst the Covid-19 lockdown. Major automakers have either suspended their production or are operating with minimal workforce. While the country’s largest passenger carmaker Maruti Suzuki recorded 47% year-on-year fall in sales in March, Tata Motors witnessed sales decline of over 68% and 87% in passenger vehicles and commercial vehicles, respectively. Based on a study by the human resources and staffing firm Adecco, the Indian auto industry can lose up to a million jobs in the dealer ecosystem, frontline roles, and the semi-skilled category. According to a 2019 Gartner forecast, more than 740,000 autonomous-ready vehicles are expected to be added globally by 2023, registering a compounded annual growth rate of over 40%. Though the increase in the number of autonomous vehicles seems exciting, these may not actually represent sales of physical units as autonomous technology and regulations are still evolving. The current crisis may speed up R&D efforts and adoption of autonomous vehicles.
Tata Motors, Mahindra & Mahindra and Escorts are some auto majors to have had showcased their working prototypes. Also, Indian start-ups such as Fisheyebox and Flux have been working on developing technologies that support the crucial elements of autonomous driving such as sensors and navigation systems. In 2017, the Automotive Research Association of India (ARAI) partnered with Cognizant Technology Solutions to work on an internal R&D programme titled SwayamGO, and showcased a prototype of an autonomous vehicle using a standard electric passenger car.
Self-driving vehicles and robots could be deployed on the streets for automatic spraying of disinfectants to sanitise public spaces. Autonomous pods and buses may also become the norm. According to a study by UBS, with an estimated market size of $1.2 trillion by 2030, robo-taxis are projected to become the top application for driverless vehicles. UBS expects ride-sharing companies like Uber and Lyft, along with automotive giants such as GM and Volkswagen, to play a leading role. In 2019, Tesla CEO Elon Musk announced plans to compete with ride-sharing platforms by using an autonomous ride-hailing fleet of over 1 million robo-taxis.
Deployment of autonomous vehicles, especially delivery vans, has already witnessed an uptick in China, post Covid-19. Chinese search giant Baidu’s open source autonomous vehicle platform Apollo partnered with self-driving delivery start-up Neolix to deliver critical supplies and food to hospitals across China. According to a Bloomberg report, Neolix has already booked orders of over 200 vehicles in the past two months including from online mega-retailers Alibaba and JD.com. Realising the benefits of autonomous delivery vans, the Chinese government has eased restrictions and is offering to fund purchase and operation of driverless delivery vans in their jurisdictions for up to 60% of the price tag.
Apart from parcel delivery, autonomous driving technologies have found innovative applications in the healthcare diagnostics space. Since March 2020, autonomous shuttles have helped Florida’s Mayo Clinic to transport tests from its testing site to a processing laboratory. The hospital partnered with the Jacksonville Transportation Authority (JTA) along with self-driving start-ups Beep and NAVYA to initially use four attendant-less shuttles handling about 700 tests a day.
Adoption, however, may be problematic in India. Besides basic infrastructure challenges, India could face regulatory and political hurdles. But contrary to fears of job losses, autonomous vehicle technology would create thousands of high-skilled engineering jobs in the country required for the constant development and maintenance of telematics and LIDAR software, along with big data analytics and machine learning.
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