An autonomous car can sense its environment and operate without human inputs. The development of new technologies in communication and robotics has influenced the transportation industry immensely. Advancement in these technologies enabled autonomous vehicles (AV) to reduce crashes, pollution, energy consumption, and congestion. Recently, the idea of personalized automation based on human behavior has been growing. For this type of autonomous vehicle, there is a need for data associated with behaviors of the vehicle’s occupants that can be obtained via physiological sensors.
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How does an Autonomous Vehicle (AV) Work?
Typically, AVs are based on a three-phase design known as “sense-plan-act”. The main challenge is sensing the complex and dynamic driving environment. For this function, AVs are equipped with many types of sensors, cameras, radars, etc., which acquire raw data from the surrounding environment. Data is subjected to relevant software whereby it gets processed and provides recommendations for appropriate courses of action, such as lane changing, acceleration, and overtaking.
Sentech plays an essential role in the development of sensor technology for automated guided vehicles (AGVs). Radars, lidars, and ultrasonic technologies are commonly used in the development of remote and orientation sensors. More specifically, the lidar and radar sensor technologies have significantly helped to advance the AV sector. Lidar examines the surroundings using laser or infrared light, while the radar understands the surroundings using radio waves.
The Need for Physiological Sensors in AVs
Sometimes humans rely on advanced driver assistance system (ADAS) technologies in potentially life-threatening situations. Therefore, vehicle occupants must have trust in their AV to operate safely. Personalization is essential to cultivate that trust by meeting the specific preferences of individual occupants concerning comfort and safety. Developing sensors for physiological measurement is a necessary step to evaluate behavior for personalization.
AVs monitor the behavior of the driver through biological signals acquired through physiological sensors; for example, drowsiness can be detected by analyzing real-time electrocardiogram (ECG) data. Eye-tracking is another physiological signal currently used to determine the attention of drivers.
Physiological sensors could detect drowsiness, emotional state, stress, lack of attention, and distraction of the driver. These sensors aim to warn the driver from forward-collision or other forms of accidents and provide them enough reaction time to avoid such adverse events. In 2005, Toyota released a camera-based driver awareness detection system, and over the years, they implemented several advanced technologies for betterment. Other automobile companies like Tesla have broken new ground in the creation of modern and technologically equipped AV. Seeing Machines Ltd. has recently developed a commercially available infrared imaging system that can detect facial features, e.g., gaze direction and eyelid positions, from automobile drivers.
A collaboration between Toyota and the University of Michigan set out to design a non-clinical measurement device to predict anomalous heart activity in the form of atrial fibrillation. They believe this device could reduce the risk of accidents related to cardiac events.
ECG Sensors for Physiological Monitoring
ECG sensors measure the heart rates from the skin surface. In this regard, ECG electrodes have been insulated with cloth and installed in driver seats to assist non-contact measurement. Another method of measuring the heart rate remotely is using ballistocardiography, which involves measuring the changes in body position due to blood pumping.
In the case of highway driving, changing lanes requires multitasking, such as controlling the steering wheel, maintaining the speed, and using relevant indicators. Scientists have measured the stress level of the drivers while changing lanes using an ECG sensor. They found increased mental stress was associated with increased heart rate, a reduction of heart rate variability, and changes in T wave ECG signals.
EEG Sensors and Autonomous Vehicles
Recently, low-cost wireless electroencephalogram (EEG) headsets have been commercially available for use in automotive research. However, researchers found that other signals generated via eye movements and muscle activities interfere with EEG output.
Thermography, in conjunction with EEG, is used to evaluate the emotional state of the driver. These sensors form thermal images of the face of the driver and non-invasively determine their emotional status. The thermography sensor requires a longer time, in comparison to EEG, to respond to the emotional changes of the driver.
Platooning and PPG Sensors
Platooning controls the distances between automated vehicles and addresses external conditions such as carbon dioxide emissions or roadway traffic. The stress encountered by the driver while following the preceding vehicle is dependent on the individual's temperament. Researchers have performed driving simulations based on electromyography (EMG) of the muscle and palm perspiration to predict the driver's stress level during platooning vehicles.
Photoplethysmography (PPG) is another sensor that is used to measure the stress levels of drivers. The sensor is attached to drivers' index fingertips, which provides the pulse rate variability and power spectrum density (PSD). Electrodermal Activity (EDA) is also used to measure stress. However, it is extremely sensitive to hand movements.
Autonomous Vehicles: Software and Sensor Technology in Driverless Cars
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Future Research in Sensors for Autonomous Vehicles
Scientists are working hard to develop more compact and wireless sensors to obtain ECG, EEG, and EMG signals. Besides sensors that monitor facial features, not many physiological sensors are commercially available. Therefore, there is a need to develop more monitoring systems for vehicle occupant monitoring that will eventually help in personalization. Also, physiological sensor technology has been mostly applied for emergency driving scenarios, but researchers believe that these sensors must also be targeted towards non-emergency applications for furthering their reach for improving personalization.
References and Further Reading
Nacpil, C.J.E. et al. (2021) Application of Physiological Sensors for Personalization in Semi-Autonomous Driving: A Review. IEEE Sensors Journal, 21(18), pp. 19662-19674. Available at: https://doi.org/10.1109/JSEN.2021.3100038
Sentech. (2021) Autonomous driving takes off thanks to sensor technology. [Online] Available at: https://www.sentech.nl/en/news/autonomous-driving-takes-off-thanks-to-sensor-technology/
Rosique, F. et al. (2019) A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research. Sensors, 19(3), pp. 648. Available at: https://doi.org/10.3390/s19030648