science

Pedestrian crash avoidance mitigation

Pedestrian crash avoidance mitigation is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Pedestrian crash avoidance mitigation rather than just read about it. In short: Pedestrian crash avoidance mitigation (PCAM) systems (USDOT Volpe Center), also known as pedestrian protection or detection systems, use computer and artificial intelligence technology to recognize pedestrians and bicycles in an automobile's path to take action for safety. PCAM systems are often part of a pre-collision system available in several high end car manufacturers, such as Volvo and Mercedes and Lexus, and…

Key takeaways

Pedestrian crash avoidance mitigation belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Pedestrian crash avoidance mitigation to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Pedestrian crash avoidance mitigation from memory before moving on to harder problems.

Reference excerpt

Pedestrian crash avoidance mitigation (PCAM) systems (USDOT Volpe Center), also known as pedestrian protection or detection systems, use computer and artificial intelligence technology to recognize pedestrians and bicycles in an automobile's path to take action for safety. PCAM systems are often part of a pre-collision system available in several high end car manufacturers, such as Volvo and Mercedes and Lexus, and used less widely in lower end cars such as Ford and Nissan. As of 2018 using 2016 data, more than 6,000 pedestrians and 800 cyclists are killed every year in the US in car crashes. Effective systems deployed widely could save up to 50% of these lives. More than 270,000 pedestrians are killed every year in the world. An excellent analysis of technology capabilities and limitations is provided in Death of Elaine Herzberg. Pedestrian safety has traditionally taken a secondary role to passenger safety.

Availability Typically, PCAM systems are part of the technology in self-driving cars and use an integrated forward-facing camera and radar or lidar system designed to help mitigate or avoid a frontal crash. However, PCAM technologies do not require self-driving technologies, just cameras and radar. Sometimes, these can be enhanced with the addition of low-light detection for pedestrians and bicycles. In 2016, the U.S. Department of Transportation's National Highway Traffic Safety Administration officially announced that automakers in the U.S. have to include the autonomous emergency braking system as a standard feature for all cars and trucks by 2022: this is a key component of PCAM. A detailed explanation for manufacturers offering emergency braking as part of a pre-collision system and often PCAM is provided as part of a broader collision avoidance system.

Functions Under certain conditions, if the PCAM systems determine that the possibility of a frontal crash with a pedestrian or bicyclist is high, it prompts the driver to take evasive action and brake by using an audio and visual alert. If the driver notices the hazard and brakes, the system may use some sort of brake assist to provide additional braking force. If the driver does not brake in a set time and the PCAM determine that the risk of collision with a pedestrian or bicycle is extremely high, the system may automatically apply the brakes, reducing speed to help mitigate the impact or avoid the collision entirely if possible. Usually, this is a setting the driver must make to initiate earlier, but it can be the default.

Technology In order to recognize a pedestrian, the computational system uses AI pattern recognition technology that typically uses machine learning and deep convolutional neural networks based on millions of images. In a simplified description, images from the car's camera and radar are compared to the prototypes stored in the computer. If a match is made and confirmed, the other systems in the PCAM are invoked. PCAM technologies can be improved with additional information from connected vehicles. A thorough description of the processes for pedestrian detection in about 2010 is provided in [1]. AI technologies have improved dramatically since then, as can be seen in an update in May 2016.

PCAM systems as part of ADAS PCAM extend the pedestrian safety systems achieved through pedestrian safety through vehicle design with automated ADAS. Volvo had the first automated braking system focused on other cars, but including pedestrians in 2009. The Insurance Institute for Highway Safety (IIHS) has published the results of their tests for pre-collision automated ADAS and determined a 50% improvement with automated braking. They did not provide separate information for pedestrian safety. HLDI, a part of IIHS, provides some evaluations of most of the main pre-collision ADAS. They found that Subaru's Eyesight I PCAM cut insurance claims by 31% and its version II, by 40%.

References

External links https://web.archive.org/web/20181005194927/http://telematicswire.net/bosch-develops-adas-system-to-prevent-car-pedestrian-collisions/ https://web.archive.org/web/20181006000215/http://telematicswire.net/2019-lexus-ux-has-advanced-features/ https://rosap.ntl.bts.gov/view/dot/12475 https://www.subaru.com/engineering/eyesight.html

Worked examples

Example 1 — a first encounter with Pedestrian crash avoidance mitigation

Start with the simplest possible case. Write down what Pedestrian crash avoidance mitigation claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Pedestrian crash avoidance mitigation before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Pedestrian crash avoidance mitigation ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Pedestrian crash avoidance mitigation

In research: Pedestrian crash avoidance mitigation appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Pedestrian crash avoidance mitigation in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Pedestrian crash avoidance mitigation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Pedestrian safety, Self-driving cars, so understanding it makes those chapters shorter.
In everyday life: Look for Pedestrian crash avoidance mitigation outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Pedestrian crash avoidance mitigation in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Pedestrian crash avoidance mitigation means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Pedestrian crash avoidance mitigation out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Pedestrian crash avoidance mitigation in simple terms?

Why does Pedestrian crash avoidance mitigation matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Pedestrian crash avoidance mitigation?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Pedestrian crash avoidance mitigation.