As ADAS technology extends to critical, time-sensitive applications – such as emergency braking, front-collision warning and avoidance, and blind-spot detection – combining data from multiple sensors enables reliable, real-time decisions for safer autonomous driving. From reading road signs to keeping you inside lane markers, artificial [2]. It is expected that the fully autonomous vehicles of the near future will be limited to a set of highly controlled settings and low speed environments [3]. The terms “Driverless vehicles” and “Autonomous vehicles” will be used interchangeably throughout this paper. An autonomous vehicle is composed of three major technological Details. Camera Recording Tips for Autonomous and Advanced Driving Assistance Systems. When acquiring video data from a camera for the development of autonomous vehicles (AV) or advanced driving assistance systems (ADAS), there are several considerations. Consider the basic functions used in recording camera data shown in flow chart in Figure 1 . Mainly two types of cameras are used in AV: Monocular and Stereo Camera. Monocular cameras provide 2D array pixels that contain detailed information about the car environment. One disadvantage of this type of camera is the lack of depth detection capability, and this information is used to determine the object's size and location on the surface. Automotive cameras can be furthermore embedded with advanced computer vision algorithms, for real Machine Vision systems, giving a disrupting contribute to the Advanced Driver Assistance Systems (ADAS). The increasingly stringent government regulations are boosting the adoption of automotive cameras both in light and heavy commercial vehicles Last year, Intel spent $15.3 billion to buy Mobileye, which makes cameras and chips for cars. In 2016, Qualcomm made a $44 billion bid for NXP, the world’s largest maker of automotive chips, but LMNy. With a whole bunch of active sensors at a given intersection, there’s a lot of noise generated affecting the ability to read the sensor signals. The thermal stereo sensor isn’t sending out MMW radar plays an important role In the driving assistance and autonomous driving. According to MMW radar’s low cost and robust working conditions, it has been widely applied on Levels 1 and 2 of driving automation defined by SAE (Society of Automotive Engineers) [ 4] and has already been used on production vehicles. The three major sensors used by self-driving cars work together as the human eyes and brain. These sensors are cameras, radar, and lidar. Together, they give the car a clear view of its environment. They help the car to identify the location, speed, and 3D shapes of objects that are close to it. An autonomous car is one of the most complex technologies that an ordinary citizen can own. It is a powerful combination of electronics, innovative software, networking, and mechanical parts. Intricate electronic sensors go hand in hand with complex software algorithms to control its autonomous car and its functioning and communication with But with the rise of automotive IoT and advances in autonomous cars, ultrasonic vehicle data is more valuable than ever. The market for automotive ultrasonic sensors was valued at over $3.4B in 2019, and is expected to grow to over $6B by 2030. This growth will coincide with the value that ultrasonic data provides for advances in urban parking When applied to autonomous vehicles where the on-vehicle camera moves as the vehicle goes, these methods would lose efficieny. Ref. [7] used statistic information in HSV color space to obtain color thresholds, they use these thresholds to do image segmentation, and then the machine learning algorithm was applied for classifying.

cameras used in autonomous cars