IEEE Consumer Electronics Magazine - September 2018 - 19

happening altogether. Active systems are one of the main
areas of interest and have seen major growth in today's vehicles. Examples of such systems include lane keeping, automatic braking, and adaptive cruise control. These systems are
commonly known as ADASs and are becoming increasingly
popular as a way for automotive manufacturers to differentiate their offerings while promoting consumer safety.
Recent studies from the World Health Organization indicate
that 1.25 million deaths occur every year due to road traffic
accidents [1]. Moreover, such accidents in recent years have an
annual global cost of US$518 billion, which takes away
approximately 1-2% of gross domestic product from all of the
countries in the world [2]. These high fatality rates, monetary
losses, and increasing customer demand for intelligent safety
systems are some of the key reasons for OEMs to develop
ADASs. Moreover, with the increasing number of electronic
control units and integration of various types of sensors, there
are now sufficient computing capabilities in vehicles to support
ADAS deployments. The different types of sensors, such as

cameras, lidar, radar, and ultrasonic sensors, enable a variety of
different ADAS solutions. Among them, the vision-based
ADAS, which primarily uses cameras as vision sensors, is popular in most modern-day vehicles. Figure 1 shows some of the
state-of-the art ADAS features and the sensors used to implement them.
Modern-day ADASs are also key technologies to realize
autonomous vehicles [3]. But several challenges with the
design, implementation, and operation of ADASs remain to be
overcome. Some of these challenges include minimizing energy
consumption, reducing response latency, adapting to changing
weather conditions, and security. In this article, we provide a
synopsis of the landscape of ADAS research and development
to address these challenges.

ADAS TAXONOMY
We propose a taxonomy of ADASs based on the type of sensors they use (Figure 2), as discussed next.

VISION SENSORS
Cameras are the most commonly used vision sensors in vehicles. Vision-based ADAS uses one or more cameras to capture images and an embedded system to detect, analyze, and
track different objects in them. In high-end ADAS, cameras
are used to monitor both the inside and outside of the vehicle.
Camera integration in modern vehicles is becoming more
common because of its low cost and easy installation. At the
2018 Consumer Electronics Show, Mobileye stated that it is
introducing smart cameras in millions of cars hitting the
streets in 2018. In addition, laws such as [4] (that mandate all
vehicles manufactured from 1 May 2018 onward use visionbased ADAS) will further aid in camera integration. Cameras
capture information such as color, contrast, and texture,
which gives them a unique advantage over other sensors. Two
types of cameras are often used in vision-based ADAS: 1)
monocular and 2) stereo.

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MONOCULAR CAMERAS
These camera systems have only one lens. As these systems
have only one image output at any point of time, they have
low image-processing requirements compared to those of
other camera types. These cameras can be used for multiple
applications, such as the detection of obstacles, pedestrians,
lanes, and traffic signs [5]. They can also be used for monitoring the driver inside a vehicle, e.g., for face- and eyedetection and head-pose analysis [23]. But monocular camera
images lack depth information and are, therefore, not reliable
sensors for distance estimation. Some techniques [5] allow
approximating distance by identifying key features in the
captured image frame and tracking their position when the
camera is in motion.

STEREO CAMERAS
These systems consist of two or more lenses, each with image
sensors, separated by a certain distance (known as stereo
base). Stereo cameras are useful in extracting three-dimensional
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Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - September 2018