How Automated Driving Systems Are Transforming Vehicle Technology

Automated Vehicles: A Transformative Era 

Autonomous vehicles can sense their environment and operate without any intervention from human drivers or passengers. The transportation industry continues to move towards AI adaptation through self-driving vehicles equipped with autonomous navigation capable of making real-time decisions during the drive spontaneously. With the advent of AI, vehicles such as self-driving cars, utility vehicles, trucks etc. can lower the fatality rate related to accidents, thus ensuring a secure and efficient mode of transportation. The levels of automation in autonomous driving systems ranging from Level 0 to Level 5 are defined by the Society of Automotive Engineers (SAE), and adopted by the Department of Transportation, U.S. For the first three levels, the human monitors the driving environment, while in the latter three, the automated system can monitor it. 

Level 0 (No Automation): The human manually controls the vehicle and performs all driving tasks such as steering, acceleration, braking etc. 

Level 1 (Driver Assistance): A single automated system is deployed in the vehicle, like speed monitoring through cruise control, etc. 

Level 2 (Partial Automation): The vehicle features the Advanced Driver-Assistance System (ADAS) which enables it to perform automated steering and acceleration, while the human driver needs to monitor all tasks and is able to control any action at all times, whenever required. 

Level 3 (Conditional Automation): The vehicle gains environment and vehicle detection system and is able to perform almost all of the tasks related to driving, yet human supervision or override may be required. 

Level 4 (High Automation): The vehicle performs all driving tasks under a certain specific set of circumstances. It requires geofencing and human override can be implemented as an option. 

Level 5 (Full Automation): Human attention and interaction are no longer required by the autonomous driving systems for performing driving tasks under any condition, area or circumstance. 

As per the Department’s National Motor Vehicle Crash Causation Survey (NMVCCS) released through the National Highway Traffic Safety Administration (NHTSA) authority in 2020, about 94% of road accidents are caused by human errors, such as impaired vision, hearing, driving under intoxication etc. This can be reduced as self-driving cars are equipped with sensors and algorithms for environmental understanding, obstacle identification and quick decision making on complex routes, resulting in a safer trip. As of 2024, the ADAS market is valued at US $58 billion across the globe and is projected to reach a value of US $125 billion by 2029. This includes commonly adopted features such as automated lane keep assistance (LKA), emergency brakes, parking, and adaptive cruise control (ACC). 

Increasing market size of autonomous driving ADAS from 2024 to 2029 

How do Autonomous Car Systems Work? 

As now we have discussed SAE levels, it is also important to note the differences between autonomous and automated vehicles, since autonomous implications are not limited to electromechanical platforms. Autonomous cars are self-aware and can take driving-related decisions on their own, for example, finding and traversing across the quickest way to reach a hospital, instead of the usual route fed by the driver on the map due to traffic conditions. On the other hand, an automated car would follow the driver’s instructions and take the same expected route while driving on its own. A third term self-driving, is usually used interchangeably with autonomous, though the difference lies in the fact that self-driving cars require the presence of a human passenger at all times, for example, BMW 7, Audi Aicon etc. These types of cars fall under Level 3 or Level 4 since they are subjected to geofencing, whereas fully autonomous cars fall under Level 4 or 5, for example, Waymo One automated ride hauling or taxis, which can go to any place without the constraint of geofencing. Let us dive into the workings of ADAS autonomous driving vehicles and certain components that they communicate with:  

1. Sensors: Autonomous vehicles gather and understand input data from their environment using sensors and sources to make driving decisions. These may include Light Detection and Ranging (LiDAR) sensors, similar to drone obstacle avoidance, which measure distances to objects using laser beams. It can also include Radio Detection and Ranging (Radar) which measures the speed and distance of objects by using radio waves and is effective for detecting objects in times of poor visibility conditions. For close-range detection of nearby objects, especially during heavy traffic and parking, Ultrasonic sensors can be used. 

2. Cameras: Surround view cameras can capture visual information about the environment, road, traffic signs, lane markings, pedestrians, gestures, vehicles etc. and provide a 360-degree view. 

3. GPS & IMU: Global Positioning System (GPS) provides accurate location to determine the location of the vehicle on a high-definition maps, where maps can also provide real-time traffic information for route planning. An Inertial Measurement Unit (IMU) can measure the vehicle’s acceleration and orientation, which helps in the stability of the vehicle and navigation. 

4. Vehicle Control Unit: VCU provides extensive information on the vehicle’s internal systems to and fro ADAS such as speed, steering angle, brake, battery status etc. for better decision making and implementing real-time safety and cost-saving functions. 

5. Algorithms: Autonomous car systems utilize onboard computers and GPUs that execute Perception algorithms like OpenCV real-time object detection and sematic segmentation to process input data, perform clustering, feature extraction and understand road conditions, lane markings etc. Such algorithms basically identify and classify objects (former) or different regions of an image (latter), such as other vehicles, lanes, road features, pedestrians etc. using sensor fusion and ML models. Localization algorithms like Simultaneous  

Levels of autonomous driving ADAS solutions described by SAE 

Localization and Mapping (SLAM) build environmental map to determine vehicle’s location in it, such as in the case of UAV solutions by using GPS, sensor data and navigational maps. Path planning algorithms generate a safe path for the vehicle and include Global Planning, that determines the start point, destination, overall route, and Local Planning, that adjusts the path with respect to real-time obstacle and traffic data. Control algorithms are responsible for executing driving commands that control the vehicle’s speed, steering, braking, maintaining safe following distance from other vehicles (ACC), ensuring the autonomous car system stays in its lane (LKA) etc. as per the planned path. 

6. Commands: After processing inputs, algorithms exhibit an output in the form of communication to vehicle’s components and actuators as per the decision taken, human action recognition and driving behavior expected. It may include steering commands to the motor to adjust the planned angle, throttle commands to control acceleration, braking commands, and other alerts to notify passengers, other vehicles’ drivers, any upcoming infrastructure or remote servers for updates and coordination. The system features multiple layers of redundancy and fail safes to handle contingent situations and ensure reliable operations post integration. 

Benefits of Automated Driving Systems 

Moving forward towards the numerous advantages offered by automated vehicles such as – 

Road Safety 

One can assure the safety of vehicle passengers with features such as lane departure warning, automatic emergency braking, adaptive cruise control, lane keeping assist, vehicle taillight recognition, blind spot detection, traffic sign recognition, glare-free high-beam, pixel light, scene text recognition, intelligent parking management system etc. The system can detect obstacles, animals, pedestrians, vehicles, bicycles, bridges, school and hospital areas etc. and take the required actions to avert impending accidents and improve walkability and livability. Vehicle sensor data logs about driver behavior, drowsiness detection, road and vehicle conditions are crucial for post-event AI video analytics, legal investigation of accidents and insurance claims. 

Operational Efficiency 

As discussed earlier, these vehicles can search for more economical routes by using real-time traffic data and road conditions, thus saving energy and time. They monitor and regulate speed and acceleration to reduce energy usage and adjust braking patterns to increase the vehicle’s life expectancy. Not only does this cut transportation, fuel, repair and battery or overall vehicle maintenance costs, but it also helps authorities devise charging infrastructure development effectively. Moreover, as autonomous driving systems can efficiently park vehicles, parking lots in schools, parks, malls, markets etc. can be freed up for other uses. ADAS solutions feature onboard and offboard data mining, machine monitoring and predictive modeling based on different types of unsupervised learning and supervised learning techniques for predictive maintenance translating to cost savings. 

Source: IEA 

Expected CO2 emissions by ADAS autonomous driving vehicles from 2023 to 2035 

Eco-friendliness 

As automated vehicles are usually electric or have hybrid engines, well-to-wheel greenhouse gas emissions are reduced significantly. On top of prolonging the vehicle’s life, optimal driving behavior exhibited by these vehicles saves energy consumption during acceleration, reduces fuel discharge, and their environmental impact. As per a recent report published by the International Energy Agency, electric vehicles powered by ADAS can reduce up to 80% of carbon dioxide emissions worldwide. 

Enhanced Accessibility 

These vehicles cater to specially abled people such as those with motor or movement disorders, Ataxia, Chorea, Dystonia, Parkinson’s Disease, Multiple System Atrophy, Polio, amputation caused by lacerations, sepsis, ganglia, prosthetic legs, hand or wheelchair users, or any other condition or situation that is unfavorable for driving. For example, Tesla’s Summon feature, which allows the car to navigate through complex roads, tight pathways and reach passengers when called, and others like auto parking, voice commands based on Natural Language Processing (NLP) etc. make traveling convenient for those who cannot drive by themselves. 

Traffic Abatement 

With the emergence of vehicle-to-everything (V2X or C-V2X) wireless communication between any type or model of vehicle with automated driving system and any entity affecting the vehicle, automated vehicles can share real-time traffic data amongst themselves to pick non-congested routes, improve road safety, reduce pollution, save energy and ensure even distribution of traffic on various roads. 

Make Your Mark with KritiKal’s Vehicle Control Unit 

E-mobility solutions such as Vehicle Control Units (VCUs), Telematics Control Units (TCUs), Battery Management Systems etc. form the fundamentals of assisted driving solutions. For example, ADAS observes a pedestrian and sends a signal to the VCU to automatically slow down the vehicle, or if it notices an obstacle, speeding vehicle or accident-prone circumstance through sensors, it asks the VCU to engage in emergency braking. Another example could be the regenerative braking mechanism activated by ADAS via the VCU as the vehicle goes downhill for smarter driving etc. The VCU is responsible for regulating subsystems of the vehicle such as the electric motor’s torque, acceleration, deceleration, battery’s health, state of charge, temperature, energy and thermal efficiency, safety monitoring etc.  

Features of our Vehicle Control Unit used in autonomous driving systems 

It communicates with autonomous driving ADAS to avoid collisions through lane departure warning, automatic emergency braking in case of anomaly detection, through its sensors, cameras and algorithms. It assists in adaptive cruise control by adjusting speed based on traffic, acting according to road signs read by ADAS, assisted parallel parking and blind spot warning in car. KritiKal offers a versatile Vehicle Control Unit that can easily manage various functions of electric and hybrid vehicles such as anti-theft systems and sensor-based data processing. Experience the revolution in automotive AI by partnering with us as our dedicated team of experts specializes in the design, development and testing of automotive control units, CAN-based communication systems, embedded systems and electronics of assisted driving technologies that meet industry standards and regulations.    

The Future of Automated Driving Systems 

As this technology has gained maximum momentum in the past decade, one can expect the transportation industry to adopt AI to greater extents. The increase in environmental concerns has led to a surge in sales of electric vehicles, so much so that many countries such as Norway and many more are looking forward to their compulsion, forging a pathway for ADAS autonomous driving. This also means advancements in ADAS-related predictive maintenance, sensors, computing power, algorithms, etc. The anticipated impact of AI in the automotive industry will feature advanced safety functions, lower power consumption, more personalization options like Tesla Car Colorizer, cost savings, operational efficiency, improving mobility etc. in the near future, which are likely to transform these industries and propel growth and innovation through intelligent transportation for society in general.  

Advancements in ADAS and automated vehicles are likely to overcome their shortcomings such as expensive solutions, the right balance between range and resolution of LiDAR and Radar, interference between LiDAR signals of different vehicles, limitation of frequency range in terms of mass production of cars, inability to recognize lane markings during heavy rainfall, snow, debris, per-mile taxes, Level 5 no-steering autonomy, accidental liabilities, lack of swift real-time judgement through non-verbal communication with pedestrians crossing, emotional intelligence etc. KritiKal can assist you in overcoming these challenges and automotive firmware, ADAS-connected Android application development, app security testing, software composition analysis, ADAS and connected infotainment, MCUs, embedded vision processors, and related additional capabilities like night vision, ACC, facial recognition etc. Let KritiKal assist you in redefining this changing landscape and bringing about promising vehicle safety, efficiency and driving experiences. Please email us at sales@kritikalsolutions.com to connect with our experts. 

Siddharth Bind

Siddharth Bind has more than 16 years of experience in designing and developing embedded systems and related solutions. He currently works as a Senior Embedded Design Engineer at KritiKal Solutions. He excels in high-speed, digital, analog, and mixed-technology designs, including differential routing, impedance control, high current trace management as well as is proficient in microprocessor design with a strong grasp of Gerber generation, DFM, DFT, reverse engineering and more. He has helped KritiKal with timely delivery of projects to some major clients.