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Autonomous and self-driving cars

Introduction

The idea of self-driving vehicles dates back much further than Google’s research in the present day. The concept of an autonomous car dates back to Futurama, an exhibit at the 1939 New York World’s Fair. General Motors created the exhibit to display its vision of what the world would look like in 20 years, and this vision included an automated highway system that would guide self-driving cars. While a world filled with robotic vehicles isn’t yet a reality, cars today do contain many autonomous features, such as assisted parking and braking systems. Meanwhile, work on full-fledged autonomous vehicles continues, to make driving a car safer and simpler in the coming decades.

The first autonomous ground vehicle capable of driving on and off roads was developed by DARPA as part of the Strategic Computing Initiative beginning in 1984 leading to demonstrations of autonomous navigation by the Autonomous Land Vehicle and the Navlab.[1] The first autonomous ground vehicle capable of driving on and off roads was developed by DARPA as part of the Strategic Computing Initiative beginning in 1984 leading to demonstrations of autonomous navigation by the Autonomous Land Vehicle and the Navlab.[1] he Grand Challenge was the first long-distance competition for driverless cars in the world; other research efforts in the field of driverless cars take a more traditional commercial or academic approach.

The U.S. Congress authorized DARPA to offer prize money ($1 million) for the first Grand Challenge to facilitate robotic development, with the ultimate goal of making one-third of ground military forces autonomous by 2015. Following the 2004 event, Dr. Tony Tether, the director of DARPA, announced that the prize money had been increased to $2 million for the next event, which was claimed on October 9, 2005. The first, second, and third places in the 2007 Urban Challenge received $2 million, $1 million, and $500,000, respectively. 14 new teams have qualified in the year 2019.

History

Autonomous Car today

How do Autonomous car works

Level of Autonomous

Types of Sensor in Autonomous Driving and there locations

Ranges of Sensors

Range of Camera

Range of Radar

History of Autonomous Cars

In GM’s 1939 exhibit, Norman Bel Geddes created the first self-driving car, which was an electric vehicle guided by radio-controlled electromagnetic fields generated with magnetized metal spikes embedded in the roadway. By 1958, General Motors had made this concept a reality. The car’s front end was embedded with sensors called pick-up coils that could detect the current flowing through a wire embedded in the road. The current could be manipulated to tell the vehicle to move the steering wheel left or right.

In 1977, the Japanese improved upon this idea, using a camera system that relayed data to a computer to process images of the road. However, this vehicle could only travel at speeds below 20 mph. The improvement came from the Germans a decade later in the form of the VaMoRs, a vehicle outfitted with cameras that could drive itself safely at 56 mph. As technology improved, so did self-driving vehicles’ ability to detect and react to their environment.

Autonomous Cars Today

At present, many vehicles on the road are considered to be semi-autonomous due to safety features like assisted parking and braking systems, and a few can drive, steer, brake, and park themselves. Autonomous vehicle technology relies on GPS capabilities as well as advanced sensing systems that can detect lane boundaries, signs and signals, and unexpected obstacles. While the technology isn’t yet perfect, it’s expected to become more widespread as it improves, with some predicting that up to half of the automobiles rolling off of assembly lines worldwide will be autonomous by 2025. Dozens of states already have legislation on the books concerning the use of autonomous vehicles in preparation for when this technology is commonplace.

Autonomous vehicles are expected to bring with them a few different benefits, but the most important one is likely to be improved safety on the roads. The number of accidents caused by impaired driving is likely to drop significantly, as cars can’t get drunk or high as human drivers can. Self-driving cars also don’t get drowsy, and they don’t have to worry about being distracted by text messages or by passengers in the vehicle. And a computer isn’t likely to get into an accident due to road rage. A 2015 National Highway Traffic Safety Administration report found that 94 percent of traffic accidents happen because of human error: By taking humans out of the equation, self-driving vehicles are expected to make the roads much safer for all.

How Do Autonomous Cars Work?

Autonomous cars rely on sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software. Autonomous cars create and maintain a map of their surroundings based on a variety of sensors situated in different parts of the vehicle. The multi-purpose camera scans the road, processing information about what it sees with remarkable speed. Responding to an obstacle in its path, the car brakes hard. Radar sensors monitor the position of nearby vehicles. Video cameras detect traffic lights, read road signs, track other vehicles, and look for pedestrians. Lidar (light detection and ranging) sensors bounce pulses of light off the car’s surroundings to measure distances, detect road edges, and identify lane markings. Ultrasonic sensors in the wheels detect curbs and other vehicles when parking.

Sophisticated software then processes all this sensory input, plots a path, and sends instructions to the car’s actuators, which control acceleration, braking, and steering. Hard-coded rules, obstacle avoidance algorithms, predictive modeling, and object recognition help the software follow traffic rules and navigate obstacles.

Every sensor and system has its limitations and advantages.

In ADAS, redundancy is important, hence sensor fusion. Most ADAS use a combination of optical and radar sensors to detect objects in the environment. To overcome the limitations the concept of Sensor Fusion is used. By combining information from both types of sensors, the ADAS camera can more accurately identify and track objects in the environment. This means more accurate ADAS warnings and interventions, with fewer false alarms.

3d Boundry box on object Detection

Levels of Autonomous

Autonomous driving is typically categorized into different levels, ranging from Level 0 to Level 5, as defined by the Society of Automotive Engineers (SAE). These levels describe the extent to which a vehicle can operate without human intervention. Here's a breakdown of each level:

Level 0 (No Automation): The vehicle is entirely controlled by a human driver, with no automation features.

Level 1 (Driver Assistance): The vehicle has certain automated systems, such as adaptive cruise control (ACC)or lane-keeping assist,(LKS) but these systems operate independently. The driver is still responsible for most aspects of driving.

Level 2 (Partial Automation): The vehicle has combined automated functions, such as simultaneous control of steering and acceleration/deceleration. However, the driver must remain engaged and ready to take control of the vehicle at all times.

Examples of Level 2 automation include Tesla Autopilot and General Motors' Super Cruise.

Level 3 (Conditional Automation): The vehicle can manage most aspects of driving in certain conditions, but the driver must still be prepared to take control when prompted by the system. The car is capable of monitoring the environment and making driving decisions. However, the driver needs to be ready to intervene if necessary. Currently, there are no commercially available Level 3 autonomous vehicles on the market.

Level 4 (High Automation): The vehicle can perform all driving tasks under certain conditions and in specific environments without human intervention. Level 4 vehicles are designed to operate autonomously within a defined geographic area or a specific use case, such as a self-driving taxi fleet within a city. However, there might be exceptional circumstances where a human driver's assistance is required.

Level 5 (Full Automation): The vehicle is capable of performing all driving tasks in any environmental condition and on any type of road, without any human intervention required. Level 5 vehicles are fully autonomous and do not have any driving controls or provisions for human occupants to take over the driving task. They are designed to operate safely without human input, enabling occupants to relax or engage in other activities while the vehicle handles all aspects of driving.

Types of sensors used in Autonomous driving and their location

ADAS (Advanced Driver Assistance Systems) and autonomous driving rely on a combination of sensors to perceive the surrounding environment and make informed decisions. Here are some of the key sensors used in ADAS and autonomous driving systems:

Cameras: Vision-based cameras capture visual information from the environment. They are used for various tasks, including object detection, lane departure warning, traffic sign recognition, pedestrian detection, and general scene understanding. Multiple cameras may be positioned around the vehicle to provide a comprehensive view.
Lidar (Light Detection and Ranging): Lidar sensors emit laser beams and measure the time it takes for the reflected light to return, allowing for 3D mapping of the surroundings. Lidar sensors provide detailed depth information and are crucial for object detection, localization, and mapping. They can accurately detect objects and provide a rich point cloud representation of the environment.
Radar (Radio Detection and Ranging): Radar sensors use radio waves to detect objects and measure their distance, velocity, and angle. Radars are particularly useful in low-visibility conditions or for detecting objects beyond the range of cameras. They provide reliable information on the presence, speed, and relative position of objects, making them important for adaptive cruise control, collision avoidance, and blind spot detection.
Ultrasonic Sensors: Ultrasonic sensors use sound waves to measure the distance to nearby objects. They are commonly used for parking assistance, providing proximity information to avoid collisions during parking maneuvers.
Inertial Measurement Units (IMUs): IMUs combine accelerometers and gyroscopes to measure the vehicle's acceleration, orientation, and angular velocity. IMUs help determine the vehicle's position, attitude, and movement, providing essential information for navigation and control algorithms.
GPS (Global Positioning System): GPS receivers use signals from satellites to determine the vehicle's precise location and velocity. GPS data is often integrated with other sensor data to enhance localization and provide a global reference for autonomous driving systems.

These sensors work together, complementing each other's strengths and compensating for weaknesses, to provide a comprehensive perception of the environment. The data from these sensors is fused and processed by sophisticated algorithms to enable tasks such as object detection, localization, mapping, trajectory planning, and control in ADAS and autonomous driving systems.

Range and features in which sensors are used

To provide increased accuracy, reliability, and robustness under a wide variety of conditions, more than one type of sensor is often needed to be used to view the same scene. All sensor technologies have their inherent limitations and advantages. Different sensor technologies can be combined to provide a more robust solution by fusing the data from different sensors looking at the same scene, “Fusion eliminates confusion”. One example is the combination of visible sensors and radar.

Visible sensor's advantages include high resolution, the ability to identify and classify objects, as well as providing vital intelligence. However, their performance is affected by the amount of available light and weather conditions (such as fog, rain, and snow). Additional factors such as heat result in image degradation due to noise. Sophisticated image processing available on TI processors can mitigate some of this.

Radar, on the other hand, can see through fog rain, or snow, and can measure distance very quickly and effectively. Doppler radar has the added advantage of being able to detect the motion of objects. However, radar is a lower resolution and cannot easily identify objects. The fusion of visible and radar data provides a solution that is much more robust under a wide variety of conditions.

Also, the cost varies between different sensors, which influences the best choice for a particular application. For example, laser radar (LIDAR) provides very accurate distance measurement but is more expensive than a passive image sensor. As development continues, costs will decrease and eventually cars will rely on a whole variety of sensors to become aware of their environment.

On what factors the Range of the Camera depends

The range of a front camera in ADAS (Advanced Driver Assistance Systems) can vary depending on the specific camera's capabilities and design. Generally, the range of a front camera in ADAS refers to the maximum distance at which the camera can effectively capture and process visual information.

The range of a front camera in ADAS depends on several factors, including:

Field of View (FOV): The FOV of the camera determines the width of the scene that the camera can capture. A wider FOV allows for a broader view of the road and surrounding objects. Typically, front cameras used in ADAS have a horizontal FOV of around 45 to 120 degrees, enabling a significant coverage of the road ahead.
Image Sensor Resolution: The resolution of the image sensor in the camera impacts the level of detail and clarity in the captured images. Higher-resolution sensors can provide clearer images even at greater distances, enhancing the effective range of the camera.
Image Processing and Algorithms: The image processing capabilities and algorithms used in the camera system play a role in enhancing the camera's performance and extending its effective range. Advanced image processing techniques, such as noise reduction, contrast enhancement, and edge detection, can improve the clarity and visibility of objects within the camera's range.
Lighting Conditions: The range of a front camera can be influenced by lighting conditions, including variations in daylight, low-light situations, or adverse weather conditions. Cameras with advanced low-light capabilities or built-in HDR (High Dynamic Range) processing can improve visibility and extend the effective range in challenging lighting environments.

The range of RADAR (Radio Detection and Ranging) sensors used in ADAS (Advanced Driver Assistance Systems) depends on several factors. RADAR sensors are capable of detecting objects and measuring their distance, velocity, and angle by emitting radio waves and analyzing their reflections. Here are some key factors that influence the range of RADAR for ADAS:

Transmit Power: The transmit power of the RADAR sensor impacts its range. Higher transmit power allows the radar waves to travel further and provide detection at longer distances. RADAR systems with higher power output generally have an extended range compared to those with lower power.
Antenna Design: The design and characteristics of the RADAR antenna influence its range. Factors such as antenna gain, beamwidth, and directivity play a role in determining how far the radar waves can travel and how effectively they can detect objects. Antennas with higher gain and narrower beamwidth can provide a longer detection range.
Frequency of Operation: RADAR sensors operate at different frequency bands, such as millimeter-wave (mmWave) or microwave frequencies. The frequency of operation affects the range and resolution of the RADAR. Generally, higher frequency RADAR can provide better range resolution but may have a shorter maximum detection range due to higher atmospheric absorption and increased scattering effects.
Environmental Conditions: Environmental conditions, such as weather, atmospheric interference, and physical obstructions, can impact the range of RADAR. Factors like heavy rain, fog, or snow can attenuate the radar waves, reducing the effective detection range. Additionally, physical obstacles like buildings, trees, or other vehicles may reflect or block radar signals, affecting the range.
Target Characteristics: The size, reflectivity, and material properties of the detected objects also influence the range of RADAR. Larger objects or objects with high reflectivity (such as metal) are typically easier to detect at longer distances compared to smaller or low-reflectivity objects.

It's important to note that the range of RADAR sensors can vary depending on the specific sensor's design, power output, and operating conditions. Different RADAR sensors are optimized for different purposes and may have varying range capabilities. Additionally, RADAR is often used in combination with other sensors like cameras and lidar to provide a comprehensive perception system with extended range and enhanced object detection capabilities.