When it comes to the open road, it is critical to place safety first. All drivers need to make sure that they keep their focus on the road at all times. On the other hand, mistakes happen. Therefore, many drivers are investing in powerful safety tools that reduce the chances of them being involved in a motor vehicle accident. This is where collision-avoidance systems, often shortened to CAS, are useful. At the same time, anyone who believes that collision-avoidance systems are a replacement for drivers paying attention to their surroundings is sorely mistaken.
What Are Collision Avoidance Systems?
There are numerous examples of collision-avoidance systems. Some of the most common examples of collision-avoidance systems include:
- Lane departure assist warning systems
- Forward collision warning systems,
- Active and automatic braking systems,
- Blind-spot monitoring collision-avoidance systems, and
- Rearview cameras.
In many respects, collision-avoidance systems represent augmented reality infiltrating the auto industry, such as the ability to distinguish pedestrians from vehicles. There are some tools that have even expanded to autonomous speed controls that allow vehicles to stop without input from the driver.
On the other hand, some industry experts have tested CAS tools and found that performance decreases with visibility. That means they may not work properly if fog sets in, if the snow blows, or if the glare becomes too much. Furthermore, some people fear that collision-avoidance systems will be used as a replacement for driver input. Therefore, it is important to take a closer look at how CAS works.
How Do Collision Avoidance Systems Work?
The vast majority of these systems use cameras that are located on both sides of the car. CAS cameras play a role in lane departure warnings and collision avoidance. These cameras continually scan the roads, looking for painted lines and marker lines. Therefore, if the road is covered in snow or if the lines have become worn down over time, this could impact the efficacy of collision-avoidance systems. Furthermore, most cars do not warn the driver if the collision-avoidance system in the car is offline or if the tool cannot read the lane markers on the road.
Watch this video to see how collision avoidance systems work on the road.
When it comes to blind-spot monitoring (BSM), CAS tends to work using radar technology. There is a small radar signal receiver and transmitter that is located on the sides of the vehicle. Usually, it is located in the rear fender. While these tools are reliable, the utility of the radar lens cab impacted if the lens itself gets wet.
What Should Drivers Know About Collision Avoidance Systems?
Collision-avoidance systems are helpful, but drivers should not place 100% confidence in them. Instead, drivers should:
- Look at collision-avoidance systems as a safety net, not as the primary cool for motor vehicle collision avoidance.
- Make sure they pay attention to the road and avoid distracted driving.
- Understand that collision-avoidance systems cannot avoid 100 percent of potential accidents.
Therefore, while collision-avoidance systems are helpful, nobody should place total faith in this technology to keep them safe in the car. Instead, they should use their own skills and place safety first.
Emerging Technologies Poised to Reduce Vehicle Collisions
Visibility Increasing Rear and Side Cameras: Legislation requiring all new models to have rear cameras and developments in side cameras aim to reduce the likelihood of reversing accidents and eliminate blind spots, boosting safety.
Headlight Advancements for Safer Night-Time Driving: Adaptive headlights that adjust illumination as per vehicle speed and direction are becoming common, thereby improving visibility on difficult roads and dark areas to reduce accidents.
Advanced Braking and Steering: Forward-collision warning systems and automatic emergency braking can significantly reduce rear-end accidents. Similarly, continuously refined Electronic Stability Control (ESC) systems correct the driver’s steering when the car skids or loses traction, improving safety.
Integrated Sensors for Driver Alerts: Lane departure warning systems and lane departure prevention systems use integrated sensors to ensure the car remains within its dedicated lane, while apps that detect distracted driving also contribute towards collision prevention.
Parking Assistance: High-tech features like parking sensors, rear cameras, self-parking systems, and apps that help find parking spots can simplify the parking process.
Augmented Reality’s Influence on the Automotive Industry
Defining Augmented Reality (AR): AR uses layers of data over visuals to provide a computer-generated perceptual experience. It has already found applications in the creation of interactive mobile apps, wearable devices, and navigational tools. Industry experts have designated five specific levels of autonomy in relation to self-driving cars.
- Level 0: Major systems controlled by human
- Level 1: Some automation that handles one system at time, like cruise control
- Level 2: Two automated systems at once (ie: steering and acceleration) but requires human control for safety
- Level 3: Ability to handle “dynamic driving tasks” but still may require driver intervention
- Level 4: Driverless or autonomous in most driving situations, but not all
- Level 5: Completely driverless or autonomous in all driving situations, no humans required
AR’s Role in Enhancing Automotive Navigation & Safety: Beginning with Heads-Up Display (HUD) systems that provide critical navigation data directly to drivers, AR has substantially improved safety and convenience. Advanced AR displays are capable of delivering turn-by-turn directions, spotting pedestrians and cyclists, and even warning about road hazards, effectively keeping drivers’ attention focused on the road. Potential years could bring about 3D HUD technology, eye-tracking augmentation, and closer integration with CAS to enhance road safety further.
How AR Improves Car Diagnostics and Maintenance: Apps powered by AR assist with diagnosing car problems, facilitating routine car maintenance chores like changing oil, providing visual guides right from the owner’s seat.
Augmented Car Buying: Showrooms are employing AR applications to give customers an immersive and detailed look at vehicle features, allowing for a more informed buying process.
The Future of AR in Automotive: With continued R&D in autonomous vehicles and transportation systems, AR’s integration in the industry is set to rise further in the coming years.
Case Study: How Automated Vehicles Are Being Used Locally
Recently, the PennDOT received a large grant from the United States Department of Transportation to begin testing the use of automated driving systems in work zones. It is the first initiative of this kind in the country. The goal of this project is to increase safety by integrating automated vehicles at roadway worksites, according to the Pennsylvania Department of Transportation.
The Pennsylvania Turnpike Commission, Pennsylvania State University, and Carnegie Mellon University will be collaborating in this innovative joint effort. With the objective of decreasing the number of collisions, deaths, and injuries caused in and around road work zones, it has received national recognition.
“Crashes in highway work zones have killed at least 4,700 Americans – more than two a day – and injured 200,000 in the last five years alone. If we can improve how AV’s interact with work zones, there will be significant safety benefits for the traveling public.”
– PennDOT Secretary Leslie S. Richards
Currently, work zones are dangerous for motorists and workers. In Pennsylvania, specifically, work zone accidents have increased in the last decade, according to PennDOT statistics. To begin to address this issue, PennDot and its local partners were awarded the $8.4 million grant to test the use of automated driving systems (ADS). The team of researchers will study how better connectivity, visibility, and high-definition mapping will impact their performance in work zones.
A self-driving vehicle developed by Carnegie Mellon University – known as the “birthplace of self-driving vehicles” – will be used during the testing process. Additionally, an HD roadway mapping vehicle will be provided by Penn State to assist in data collection related to location, speed, and lane designations. It’s important to note that AV testing regulations require the presence of human drivers ready to take control of the vehicle if necessary.
Testing will be done with a wide range of worksite configurations in different simulated contexts – in cities, rural and suburban areas. In the final phase, testing will be carried out in actual work zones along the roadways, including those in Delaware and Lancaster counties. Local authorities should notify residents before testing on regular roadways begins.
Over a four-year span, this project will work to verify the safety and practicality of using AV solutions in work zones. It will involve ongoing testing, simulations, and gradual integration with real drivers and machinery operators. The data collected by the team will be used to overcome the challenges currently blocking the regular use of automated vehicle technology. For example, other testing has shown that automated vehicle detection systems aren’t reliable because many work zone cues are designed for human drivers.
PennDOT aims to understand how automated vehicles can be used to make roadwork safer for operators and drivers. With improved technology, the team hopes to demonstrate how connected and autonomous vehicles impact traffic flow and safety factors in different situations. Ultimately, the solutions developed will be applied in work zones throughout the Commonwealth.
Self-Driving Car Insurance and Liability
One of the biggest questions regarding self-driving cars typically is: Who’s responsible in the event of an accident? From 2009 through 2012 the Google self-driving car project placed autonomous, Level 4 self-driving cars on the road with a test driver. These test drives included complex city street routes where the car had to compensate for a variety of situations with pedestrians, cyclists, and even road construction. As the years and technology have progressed, the risk for accidents has increased. The more self-driving cars on the road, the more miles traveled and the higher the probability some form of accident can and will occur.
So what happens if this self-driving car doesn’t stop for a pedestrian? Or it crashes into a pole while avoiding road construction? If the car is the “driver”, and the car is not human, then who is responsible? In general, in the event of an accident, it’s the manufacturers that shoulder the liability for their vehicles that are operated with little to no human intervention.
In 2015, Volvo stated that as a manufacturer, they would accept full liability in the event of a self-driving car crash. Other manufacturers were expected to follow Volvo’s lead. However, an accident that happened in May 2016 was a different story.
A driver using a Tesla autopilot system on their Tesla Model S was killed when the car didn’t brake as a truck made a left turn in front of it. The car went under the truck, drove off the road, and hit a pole. The Tesla autopilot system is considered Level 2 autonomy requiring human control for safety. After a six-month investigation by the National Highway Traffic Safety Administration, it was determined the driver was at fault, not Tesla. This particular case demonstrates that the varying levels of self-driving cars/autonomy play an important role regarding liability and car insurance.
What’s on the Horizon for Driver Safety in Pennsylvania?
The Commonwealth has always been a leader in innovation. Pennsylvania continues to fuel innovation related to the development and on-road testing of Highly Automated Vehicles (HAV) and automated guided vehicles. PennDOT has also played an active role in setting strong safety standards regarding the use of this type of technology and a federal automated vehicles policy
These local authorities plan to keep paving the way for technological research and public safety solutions. Looking ahead to 2040, for example, PennDOT is already studying how connected and autonomous vehicles can be utilized to improve the surface transportation network in the state. Their vision for 2040 lays out years of research which will be dedicated to the development of AVs, communication devices, data networks, workforce training, and the related investments needed.
PennDOT has also joined the Smart Belt Coalition to join forces with universities, technology leaders, and transportation agencies in other states. Along with PTC, Ohio DOT, the Ohio Turnpike, and Michigan DOT, they will work to create more automated and connected vehicle initiatives. The coalition supports research, testing, policy-making, funding campaigns, and integration programs.
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