Autonomous vehicles (AVs) have revolutionized the transportation industry, and embedded systems are at the core of this innovation. These systems are crucial for the safety, efficiency, and overall functionality of self-driving cars. As AVs become more advanced, the need for robust, real-time embedded systems has never been more critical.
The six levels of autonomous vehicle autonomy defined by the Society of Automotive Engineers (SAE) are:
- Level 0: No driving automation, the driver performs all tasks
- Level 1: Driver assistance, the car can control speed or steering, but not both
- Level 2: Partial driving automation, the car can control speed and steering in certain conditions
- Level 3: Conditional driving automation, the car can drive itself in certain conditions, but may ask the driver to intervene
- Level 4: High driving automation, the car can drive itself in most normal driving conditions, but may not be able to handle all scenarios
- Level 5: Full driving automation, the car can drive itself in all conditions without human input
At level 0 the driver controls all the systems whereas at level 5 the vehicle is running fully autonomously. Level 5 vehicles will not have any steering wheel, brake pedals or accelerator pedals and are expected to drive in any condition even on bad or damaged roads. Level 4 vehicles will have some of these and are usually used for autonomous taxi scenarios. You may find most of the vehicles on the road at level 1.
What Are Embedded Systems in Autonomous Vehicles?
Embedded systems in autonomous vehicles refer to specialized computing systems integrated into various parts of the car, controlling everything from sensor data processing to decision-making systems to controlling the different systems of the automobile. These systems are designed for specific tasks and are essential for ensuring the vehicle’s operational safety and driver/ passenger comfort.
Safety-Critical Features of Embedded Systems
Autonomous vehicles rely on real-time data processing to make driving decisions, such as lane changes, braking, and acceleration. Embedded systems ensure these decisions are made accurately and quickly, minimizing the risk of accidents. Some example systems are:
Antilock Braking System (ABS) with Electronic Brakeforce Distribution (EBD):
ABS is required in vehicles to make sure one can steer the vehicle while brakes are applied without fear of overturning. EBD helps in improving the efficiency of ABS by applying required braking to the 4 wheels such that stability of the vehicle is not lost.
Driver Drowsiness Detection:
This is required for vehicles at level 1-4. Cameras fitted inside the car along with sensors on the driver seat detect if the driver is getting into drowsy mode and then generate an alert to the driver to wake up and come out of the drowsiness condition.
Adaptive Cruise Control:
This is a mechanism that helps to adaptively change the speed of the vehicle based on the obstacle in the front of the vehicle. With the dash camera, ultrasonic sensors, radar sensors, LIDAR sensors the system detects the type of obstacle, its speed to make appropriate decisions on changing the speed or braking. If it happens to be a moving vehicle then the reduction will be appropriate to the speed of the vehicle but if it is a static object then both reduction of speed and braking is applied based on the distance of the object.
Important Technological aspects to build autonomous vehicles include:
Sensor Fusion: Embedded systems integrate data from multiple sensors (LiDAR, radar, cameras) to provide a complete view of the vehicle’s surroundings. This is critical because the sensors inherently could provide spurious outputs once in a while. So it is critical to rely on multiple sensors in most of the cases to make appropriate decisions like speeding or braking.
Redundancy:
For safety, embedded systems often have backup components to take over if a primary system fails. The microcontroller architecture is designed in some of the safety critical systems like ABS that an alternator processor system will either take over the functionality or the alternate processor monitors the status of a processor to provide an alert in case the processor is stuck in any unwanted state.
Real-Time Processing:
AVs need immediate responses, and embedded systems process sensor data in real-time to ensure rapid decision-making. Safety critical systems fall into hard real time systems as they could create hazards to the driver and passenger lives
Efficiency in Autonomous Vehicles:
Apart from safety, embedded systems also improve the overall efficiency of autonomous vehicles. They optimize resource allocation, reduce energy consumption, and help ensure smooth operation under varying driving conditions.
Energy Efficiency:
Embedded systems optimize power consumption, which is crucial for electric autonomous vehicles, to help in making them feasible for usage in driving longer distances. This inturn will help prolonging battery life as the battery life is directly dependent on the number of charging cycles.
Route Optimization:
Using GPS and real time traffic data, these systems find the most efficient routes, reducing fuel consumption and travel time. They can also provide convenience for the driver to make decisions like to drive through no tolls or with tolls, or to drive through certain paths as the driver wants to visit certain areas while going towards the destination.
Key Challenges:
While embedded systems significantly enhance safety and efficiency, they come with challenges. The systems need to meet strict real-time processing requirements and manage an immense amount of data, all while operating in potentially harsh environments.
Dynamic Nature of environment: The biggest challenge is the dynamic nature of the environment while driving. Especially for the systems that use Machine Learning (ML) to make decisions, sometimes the ML models may not have seen the data at all. So the system developers must be careful in deciding the level of autonomy for a specific system.
Processing Power: Autonomous vehicles generate enormous data from various sensors. Processing and making decisions based on this data require substantial computational power. You may need larger batteries to power these processors which means you need a higher amount of energy.
Heat Management: High-performance embedded systems generate a lot of heat, which can affect performance if not managed properly.
Future of Embedded Systems in Autonomous Vehicles
The role of embedded systems in AVs will continue to grow as technology advances. Future trends include increased use of AI for decision-making, more sophisticated real-time systems, and improvements in hardware that allow for more compact and powerful embedded solutions. Moreover, the rise of 5G will enable faster data processing and communication between vehicles, further enhancing safety and efficiency. With 5G providing low latency support, one could control the driving of cars remotely.
Conclusion
Embedded systems are the backbone of autonomous vehicles, enabling both safety-critical operations and efficiency improvements. As autonomous technology evolves, the role of embedded systems will become even more integral, ensuring safer roads and smarter, more efficient transportation systems.
