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June 9, 2016

The evolution of motor vehicles has accelerated over the last decade – from manual transmission automobiles to uber-sophisticated semi and fully autonomous smart cars guided by onboard sensors and autopilot systems. This has been fueled primarily by rapid innovation in, and adoption of, the intelligent sensor technology.

This is an exciting time for the auto industry as it makes its way toward disruptive driverless cars. Companies like Mercedes-Benz, Volvo, Toyota, and Google Inc. are racing to develop smart cars with rich features that deliver safer, convenient, and comfortable driving experiences. According to a NASDAQ article, driverless cars are likely to dominate the market by 2030.

However, the success of these cars will depend on the quality of information generated and distributed by the intelligent sensors embedded in them. Auto manufacturers will need to combine smart sensors and advanced analytics to create truly successful smart cars.

Intelligent Sensors in Smart Cars

Deploy intelligent sensors that can feel the surroundings

From airbags, safety belts, and bumper shocks to smart features like auto-braking, blind spot detection, and lane-departure alerts, safety has always been the core focus of the automotive industry. Intelligent sensors come with data gathering and self-diagnostics features for enhanced reliability, interoperability, and control. They play a key role in self-driving cars as they offer comprehensive sensing capabilities that help detect the environment, communicate with other vehicles, and navigate safely without human intervention. Audi has embedded 16 sensors in its Smart Car A7, making it one of the most remarkable driverless cars in the luxury car segment. The sensors help the car detect the surrounding environment including imminent dangers, understand the travel path of other vehicles on the road, and navigate its route.

Some of the key sensors used for automotive safety include radars, LiDARs, and ultrasonic and vision based sensors – LiDAR being the most robust and popularly used sensor technology. Ford recently tested LiDAR sensors in its Ford Fusion Hybrid autonomous research vehicle to successfully navigate long, lonely stretches of desert roads in complete darkness demonstrating the capability to perform beyond human limits. This is a significant development as cars will no longer need light or cameras to navigate in the dark.

While sensors are still quite expensive, the technology is evolving at a rapid pace due to the growing market demand. Manufacturers are working furiously on developing high performing, small form factor, and low-cost sensors to make the technology more accessible, and cater to varied requirements of the automobile industry.

Leverage advanced analytics to help systems think intelligently

As driverless cars and connected highways become a reality in the near future, an integrated advanced telematics and Big Data analytics platform will be key to their success. Smart cars rely on the interconnectedness of the ecosystem, and their ability to communicate with other vehicles, sensors, and microprocessors, in real time. This is where Big Data analytics comes in. For the sensors to collect, process, interpret, and transmit large amounts of data to and from a wide range of third-party systems, you need robust data analytics and modeling tools. Machine learning and predictive models help smart cars foresee the future and avoid impending crashes or road accidents.
Moreover, with the emergence of smart highways that combine roadside sensors and smart signaling systems, it is critical for vehicles to communicate with the surrounding sensor network for superior traffic management. In one such recent smart infrastructure initiative, the US Department of Transportation committed to funding up to $40 million to help a city – the winner of the Smart City Challenge – define its smart city initiatives and integrate innovative technologies. This will include integrating connected vehicles, driverless cars, and smart sensors into the citys transportation network.

Balance security and innovation to navigate the road ahead

Modern smart cars are set to revolutionize the future of mobility. A recent study shows that shared autonomous vehicles have the potential to save up to 15 to 20% of urban space. Smart cars promise to drastically improve road safety, and reduce accidents, congestion, and carbon emission. But these cars also bring a host of data security threats in their wake. Take for instance the security lapse discovered recently in the BMW connected car. The vulnerability, which has since been addressed, exposed two million vehicles to hackers who could potentially unlock car doors remotely. Such cyber security threats can impact vehicle safety. Therefore, identifying and addressing vulnerabilities early on in the development cycle is critical to ensuring that smart cars ultimately deliver on their promise and potential.

Upendra Suddamalla is currently working as Techinical Lead, Image processing in Embedded Innovation Lab of Tata Consultancy Services, Bangalore, India. He has around 11 years of R & D experience in the field of image processing, Computer Vision and Machine Learning. Upendra has 6 patents filed in this area. He did his B. Tech. in Electronics Instrumentation and Control Systems from Sri Venkateswara University, Andhraparesh, India. He is an expert in vision sensor based algorithm development for ADAS (advanced driver assistance system) and AD (autonomous driving) in TCS.


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