Autonomous Cars

Autonomous Cars

The autonomous car’s technology has the potential to change the transport industry significantly. From the perspective of technology, self-driving vehicles offer the possibility of reducing pollution, minimizing road accidents, and decreasing the congestion caused by traffic jams. In conventional cars, the human driver takes control of all the functions. While the automation technology offers different levels of the continuum, the autonomous cars take up level 4; the human-driven car takes level zero. In light of the effects of accidents, autonomous vehicle technology has the potential to minimize the frequency of crashes globally compared to human-driven cars. According to the 2010 report compiled by the Insurance Institute for Highway Safety (IIHS), the number of accidents caused by the head-on collision can be reduced by installing forward-collision warning devices and adaptive headlights. On the same note, the estimated number of car crashes in the United States 5.3 million in 2011, which resulted in approximately 2.2 million injuries (Anderson et al., 2014). In the case of the technology of the self-driving vehicle, level 4 automated cars have the potential to further reduce the accidents from human errors by a more significant percentage. Of the collisions, the report estimates that the incidences of alcohol abuse caused approximately 39 percent of the fatal car crashes. The autonomous car technology uses the concept of automatic braking to detect an obstacle and other cars to prevent a significant portion of the accidents caused by drunk driving.

In spite of the advantages of self-driving cars in the contemporary world, the barriers and perceptions of the safety of autonomous vehicles limit the adoption of vehicles (Konig & Neumayr, 2017). Human error is the leading cause of accidents in the world because of human impairments due to alcohol use and bias in perception of distance, position, and proximity to other road users. Robotics experts confirm that self-driving cars will cause accidents, but the automation technology should be adopted to reduce death due to visual perception and drunk driving. As Tovey puts it, autonomous vehicles will save more lives in the long term (2019).

The effect of self-driving cars on road safety remains significant because the frequency of human error while driving increases significantly under the influence of alcohol. The fatalities produced by drunk driving pose a public health issue that imposes a substantial use of resources in both the private and social welfares. Studies find that humans tend to believe they are better drivers than autonomous vehicles even though the opposite appears to be true. Google autonomous vehicles cars have a lower accident rate that was reported to the police between 2009-2015 (Teo). Limiting the alcohol-impaired driver reduces the rate of accidents by about one third. In this case, the automation of cars to level 4 reduces the crashes to make the technology better than level zero that gives the vehicle controls to humans. Anderson et al., (2014) asserts that motorists under the influence of alcohol killed half of the pedestrians involved in road accident in 2011. Additionally, approximately 38 percent of the cyclist who died in roads accidents were killed by human impaired drivers under the influence of alcohol. It makes sense that self-driving cars are better than human drivers because of the perfect perception are. From the perspective of perception, autonomous cars have the potential to gather sensor data and interpret the information to attain ultra-reliability to eliminate the perception bias from human impaired drivers. The vehicle technology plans the algorithms and executes them to precision by selecting the optimal course of action. The level 4 self-driving car does not get tired, like humans driving cars for long distances. More specifically, the computer algorithms evaluate, compare, and maneuver the trajectory of obstacles and other vehicles by considering the vehicle’s position and the possible outcomes within a short period. As opposed to a human impaired driver under the influence of alcohol, autonomous cars have faster reactions because the driving environment involves several elements. The factor involved in driving includes severe weather conditions, traffic events, infrastructure, other vehicles, and road users that impair the perception of human drivers. While the eyes of human drivers act as passive sensors, interpreting information such as light reflection, the judgment of distance as well as recognition of shapes complicate the interpretation of visual information.

According to Nees (2019), the inflated perception of safety presents challenges on the adoption of autonomous vehicles because of the bias. While human beings have the desire to control the automobiles by reducing the impression of passive drivers, the human limitation from alcohol impairment poses a significant risk to the road users. The discourse of safety in light of the autonomous cars on the optimal performance of the self-driving vehicles reaches beyond the scope of the human driver on accuracy. In this case, the rational decision-making lacks the probabilistic outcome generation algorithm that the autonomous cars have. The study conducted by Nees (2019) indicates that most human drivers believe that they can drive safely compared to the average driver in the better-than-average effect of perception. Similarly, the drivers believe that they were better than the contemporary self-driving cars.

For the human driver impaired by alcohol use, the reliance of sight to recognize the visual information under the aforementioned factors in the driving environment makes human-driven vehicles dangerous for road users. The computer-based camera systems in autonomous cars use optical recognition as superior to the human eyes to make the level 4 driven cars better than human counterparts. The solutions to perception challenges for the self-driving cars solve the human driver because the computer-based camera technologies do not have the limitations of long-distance judgments because the sensors can gather data from object distances to generate algorithms that incorporate the visual information in the performance of the cars. More specifically, the robotic systems of light detection enhance the field of view for the vehicles to engage the braking system for cases that involve collisions: proximity sensors.

Sensor Suites

The challenges in adopting the autonomous vehicles emanate from the perception that the human drivers have the memory of the road networks and direction beyond the field of view provided for in the automation sensors. In light of the blind spots, the technology offers the limitation based on the field of view. Unlike human counterparts, autonomous cars do not know the environment away from their field of view. For example, human drivers have the memory of sharp bends and blinds spots on the road, thus make a greater sense of the environment. As such, the autonomous vehicle technology corrects the limitation by utilizing the global positioning system (GPS) to localize their position concerning the situation. By extension, the GPS relies on the satellite data to triangulate the location of the car to the global coordinates that identify the position of the vehicle in light of the road and the environment. In other words, road networks are incorporating in the sensor suites for the self-driving cars to minimize the chances of collision in the urban areas and the road networks unknown to the human drivers. The integration of the GPS system with the inertial navigation system make the self-driving cars safer compared to the human-driven cars. The inertial navigational system consists of the accelerometers and gyroscopes coupled with the GPS calculates car orientation, position, speed, drift algorithms continuously to increase the accuracy of navigating the road networks safely. Compared to the human counterparts, level 4 driving cars take road safety to a higher level because the system maintains accurate maps and waypoints encoded in the GPS systems.

While Teoh and Kidd (2017) believe that the development of autonomous vehicles generates both challenges and safety advantages, the results of the study indicate that the overall rate of accidents that involve the self-driving cars has been smaller compared to the human-driven cars. The project of the Google self-driving cars employs the use of system controls that minimize the severity of reportable accidents in the public roads. The automation concept reduces the economic cost of accidents by limiting human impairment errors. Teoh and Kidd concluded from the results of the study that the rate of accidents for the human-driven cars was more than twice the accidents caused by self-driving cars. Most conspicuously, the sensor-based solution to the human challenges of perception and communication with objects made the autonomous vehicles avoid collisions. On the same note, the traffic management attribute of the self-driving cars coupled with the convergence communication with other road users and objects based on the analysis of the visual information makes level 4 driving safer than the human drivers. The perception of the external environment for the autonomous vehicle fuses artificial intelligence, proximity sensors, GPS sensors, and robotic camera systems to generate continuous algorithms that communicate with the environment in real-time. Otherwise, the connectivity solution that autonomous vehicle technology provides makes the self-driving cars safer on the sophisticated road networks compared to human drivers. Moreover, human impairment due to alcohol use increases the chances of an accident by a high probability. This is because the human driver uses eyesight as a passive sensor that is prone to fatigue as opposed to the integrated communication and visual analysis of the automated computer systems in the self-driving cars.

The studies conducted on self-driving cars show that the adoption of autonomous vehicles has the potential to save human lives on road networks. Statistically, autonomous vehicles eliminate the errors caused by human impairment due to alcohol use and carelessness. From an emotional perspective, the self-driving technologies gather data and information to generate continuous algorithms in real-time to calculate probabilistic outcomes that enable the computer to take a safer course of action. Indeed, the sensor-based solution to the human challenges of perception and communication with objects made the autonomous vehicles avoid a collision. Similarly, the traffic management attribute of the self-driving cars coupled with the convergence communication with other road users and objects based on the analysis of the visual information makes level 4 driving safer than the human drivers. While the adoption of autonomous vehicles presents several challenges in light of the safety, the concept that eliminates the problem of human impairment due to alcohol use and visual perception bias produces significant insights into the road safety parameters.

References

Nees, A. N. (2019), Safer than the average human driver (who is less safe than me)? Journal of Safety Research, 69, 61-68. doi: 10.1016/j.jsr.2019.02.002

Teoh, E. R., & Kidd, D. G. (2017). Rage against the machine? Google’s self-driving cars versus human drivers. Journal of Safety Research, 63, 57-60. doi: 10.1016/j.jsr.2017.08.008

Konig, M., & Neumary, L. (2017). Users’ resistance towards radical innovations: the case of the self-driving car. Science Direct, 44, 42-52. doi: 10.1016/j.trf.2016.10.013

Katherine, H. (2015). Old laws, new tricks: Drunk driving and autonomous vehicles. The Journal of Law Science & Technology, 55(2), 275-289. Retrieved from: http://eds.a.ebscohost.com/

Tovey, A. (2019). Self-driving cars will kill – but also save lives, says Toyota boss. Telegraph Online, 23. Retrieved from: https://link.gale.com/

Anderson, J. M., Nidhi, K., Stanley, K. D., Sorensen, P., Samaras, C., & Oluwatola, O. A. (2014). Autonomous vehicle technology: A guide for policymakers. Rand Corporation.