6.2 KiB
| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Vehicular automation | 2/7 | https://en.wikipedia.org/wiki/Vehicular_automation | reference | science, encyclopedia | 2026-05-05T04:24:39.053882+00:00 | kb-cron |
Ford offers Blue Cruise, technology that allows geofenced cars to drive autonomously. Drivers are directed to stay attentive, and safety warnings are implemented to alert the driver when corrective action is needed. Tesla, Incorporated has one recorded incident that resulted in a fatality involving the automated driving system in the Tesla Model S. The accident report reveals the accident was a result of the driver being inattentive and the autopilot system not recognizing the obstruction ahead. Tesla has also had multiple instances where the vehicle crashed into a garage door. According to the book "The Driver in the Driverless Car: How Your Technology Choices Create the Future," Tesla automatically performs an update overnight. The morning after the update, the driver used his app to "summon" his car, and it crashed into his garage door. Another flaw with automated driving systems is that unpredictable events, such as weather or the driving behavior of others, may cause fatal accidents due to sensors that monitor the surroundings of the vehicle not being able to provide corrective action. To overcome some of the challenges for automated driving systems, novel methodologies based on virtual testing, traffic flow simulation and digital prototypes have been proposed, especially when novel algorithms based on Artificial Intelligence approaches are employed which require extensive training and validation data sets. Implementing automated driving systems poses the possibility of changing built environments in urban areas, such as expanding the suburban regions due to the increased ease of mobility.
== Challenges == Around 2015, several self-driving car companies including Nissan and Toyota promised self-driving cars by 2020. However, the predictions turned out to be far too optimistic. There are still many obstacles in developing fully autonomous Level 5 vehicles, which is the ability to operate in any conditions. Currently, companies are focused on Level 4 automation, which is able to operate under certain environmental circumstances. There is still debate about what an autonomous vehicle should look like. For example, whether to incorporate lidar to autonomous driving systems is still being argued. Some researchers have come up with algorithms using camera-only data that achieve the performance that rival those of lidar. On the other hand, camera-only data sometimes draw inaccurate bounding boxes, and thus lead to poor predictions. This is due to the nature of superficial information that stereo cameras provide, whereas incorporating lidar gives autonomous vehicles precise distance to each point on the vehicle.
=== Technical challenges === Software Integration: Because of the large number of sensors and safety processes required by autonomous vehicles, software integration remains a challenging task. A robust autonomous vehicle should ensure that the integration of hardware and software can recover from component failures. Prediction and trust among autonomous vehicles: Fully autonomous cars should be able to anticipate the actions of other cars like humans do. Human drivers are great at predicting other drivers' behaviors, even with a small amount of data such as eye contact or hand gestures. In the first place, the cars should agree on traffic rules, whose turn it is to drive in an intersection, and so on. This scales into a larger issue when there exists both human-operated cars and self-driving cars due to more uncertainties. A robust autonomous vehicle is expected to improve on understanding the environment better to address this issue. Scaling up: The coverage of autonomous vehicles testing could not be accurate enough. In cases where heavy traffic and obstruction exist, it requires faster response time or better tracking algorithms from the autonomous vehicles. In cases where unseen objects are encountered, it is important that the algorithms are able to track these objects and avoid collisions. These features require numerous sensors, many of which rely on micro-electro-mechanical systems (MEMS) to maintain a small size, high efficiency, and low cost. Foremost among MEMS sensors in vehicles are accelerometers and gyroscopes to measure acceleration around multiple orthogonal axes—critical to detecting and controlling the vehicle's motion.
=== Societal challenges === One critical step to achieve the implementation of autonomous vehicles is the acceptance by the general public. It provides guidelines for the automobile industry to improve their design and technology. Studies have shown that many people believe that using autonomous vehicles is safer, which underlines the necessity for the automobile companies to assure that autonomous vehicles improve safety benefits. The TAM research model breaks down important factors that affect the consumer's acceptance into: usefulness, ease to use, trust, and social influence.
The usefulness factor studies whether or not autonomous vehicles are useful in that they provide benefits that save consumers' time and make their lives simpler. How well the consumers believe autonomous vehicles will be useful compared to other forms of transportation solutions is a determining factor. The ease to use factor studies the user-friendliness of the autonomous vehicles. While the notion that consumers care more about ease to use than safety has been challenged. It still remains an important factor that has indirect effects on the public's intention to use autonomous vehicles. The trust factor studies the safety, data privacy and security protection of autonomous vehicles. A more trusted system has a positive impact on the consumer's decision to use autonomous vehicles. The social influence factor studies whether the influence of others would influence consumer's likelihood of having autonomous vehicles. Studies have shown that the social influence factor is positively related to behavioral intention. This might be due to the fact that cars traditionally serve as a status symbol that represents one's intent to use and his social environment.
=== Regulatory challenges ===