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Beyond the Driver’s Seat: Unraveling the Potential of Autonomous Vehicles
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NavigationAutonomous Vehicles (AVs), commonly called Self-driving Vehicles, will give a new perception to transportation and our emerging lifestyle. These vehicles can operate without human intervention and collect data using sensors like Cameras, LiDAR, RADAR, and Ultrasonic Sensors.
Autonomous vehicles rely on advanced Artificial Intelligence (AI) and Machine Learning (ML) systems to sense their environment and take further actions. Sophisticated sensors and actuators are utilized in conjunction with advanced computer vision features to:
- Continuously update the map of the vehicle’s surroundings
- Detect the presence of nearby vehicles and pedestrians
- Measure distances
- Detect uneven surfaces in roads and sidewalks
AVs can also be interconnected with other external devices such as smart traffic lights, roads, and vehicles.
SDVs (Software-Defined Vehicles), in the context of ADAS (Advanced Driver Assistance Systems), play a significant role in fostering their growth and adoption. SDVs typically have powerful onboard computers. Hence, integrating them with ADAS can have several benefits, including improved scalability and flexibility in deploying new over-the-air updates and features, reduced computational power to process data, sensor integration, data collection, learning, testing, and validation, etc. With rigorous testing, adherence to safety standards, and addressing potential challenges like cybersecurity threats, the combination of ADAS and SDVs holds considerable promise for defining the future of safer and more effective transportation.
In this blog, we will discuss the benefits of Autonomous Vehicles, different levels of Autonomy in automotive, Dataset Testing for self-driving cars, and the Future of AVs.
Levels of Autonomy
Most commercially available autonomous vehicles are currently at L2 or L3 stage. Achieving Level 5 Automation on a large scale involves addressing complex technological, regulatory, and safety challenges, and it may take time before such vehicles become commonplace on our roads.
Sensors in Autonomous vehicles
- LiDAR (Light Detection and Ranging) - Provides 3D info by sensing the surrounding. Mainly used for measuring distance, detecting objects, lane markings, etc.
- Camera - Provides 2D image data which can be used to classify labels of objects, traffic signals, etc.
- RADAR (Radio Detection and Ranging) - Uses radio waves to determine the distance between objects.
- Infrared Sensor Technology - Uses heat waves to detect objects in low light conditions
- INS (Inertial Navigation System) - Uses GPS to calculate a vehicle’s position, orientation, and speed as well as to increase location accuracy.
- Prebuilt Mapping Technology - This provides routes that can be traveled which are restricted by prebuilt mapping technology and uses offline maps that have already been created.
- Ultrasonic Sensor Technology - For parking and backing warnings, ultrasonic sensor technology offers information at a close range.
- GPS (Global Positioning System) - Uses a Satellite to transmit the vehicle’s position data.
After processing all the sensor inputs, sophisticated AI-powered software will issue commands to the car’s actuators so they may map routes, manage steering, braking, and acceleration, or avoid obstacles.
In the Perception module, the algorithm uses data collected from sensors and aids in detecting and tracking objects (like cars, pets, animals, and statics), object classification, finding free and occupied space using an occupancy grid map, lane markings, traffic signs, etc. For better results, data collected from different sensors can be combined at the same timestamps.
Another major module is Guidance, Navigation, and Control (GNC). Based on the environmental map created by the perception system, the GNC system isolates the drivable region, performs localization, path, and motion planning, and issues the required high-level control commands to the drive-by-wire system.
Dataset and Testing:
Testing Autonomous Vehicles is a critical step in development to ensure they can operate safely and reliably in real-world conditions. The process includes gathering data, building datasets, and conducting various tests to evaluate the vehicle’s performance. Key aspects of testing include Data Collection, Annotation, Dataset building, Simulation, Training and Validation, testing scenarios, safety validation, regulatory compliance, and real-world testing.
There are different open-source data sets available to test AV software. A few of them would be Kitti, Nuscenes, Lisa, Waymo, etc. There is also another option to collect/record the data from the sensors mounted on the test vehicle to test the software of self-driving cars.
Leading OEMs and Suppliers are actively working on making level 5 autonomous vehicles. Waymo has tested its vehicles by driving over 20 million miles on public roads and tens of billions of miles in simulation. Tesla has driven over 3 billion miles in Autopilot mode since 2014. Likewise, Cruise (GM), Aptive, Uber, Baidu, Zoox, Mobileye, etc. are on the verge of testing.
