/How Autonomous Vehicles Mitigate T-Bone Collisions

How Autonomous Vehicles Mitigate T-Bone Collisions

By Sidhart Krishnamurthi, Product Management @ Recogni

Imagine this common scenario – you’re driving in your neighborhood, approaching a four-way stop sign. You stop and check your surroundings, and just as you begin to accelerate, you see another car coming toward you, fast, making an illegal left turn! By the time you’re able to brake, you’re in a T-bone collision.

T-bone collisions have dire consequences. Annually, they kill an estimated 8,000 to 10,000 people – more than both head-on crashes and incidents with vulnerable road users (VRUs) combined. T-bone collisions account for over 50% of all car accidents. Because of the nature of this crash type, survivors face debilitating neck, back, and spinal injuries. Clearly, there needs to be a change.

T-bone collisions stem from the non-zero human reaction time (1.5 seconds) which prevents drivers from making a safe maneuver. Take a look at the three diagrams below, representing a common four-way stop in broad daylight:

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Figure 1 – T-Bone Collision – the Set-up
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Figure 2 – T-Bone Collision – The Oncoming Car Prepares to Turn
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Figure 3 – T-Bone Collision Scenario – Impact

Because the driver of the car can’t react in time, they are caught in a T-bone accident. This everyday scenario does not take into account other exacerbating factors, such as bad weather, night driving, or distracted driving. All of this necessitates an even quicker reaction time, contributing to the dire statistics outlined above.

Fortunately, autonomous vehicles (AV) provide a solution for these concerns. What if you were in an AV when the other car took a dangerous turn into your lane?

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Figure 4 – T-Bone Collision Avoidance by AV – The Setup
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Figure 5 – T-Bone Collision Avoidance by AV – The Oncoming Car Prepares to Turn
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Figure 6 – T-Bone Collision Avoidance by AV – The Oncoming Car Turns but the Collision is Avoided

In the scenario above, when the AV notices the oncoming vehicle, it immediately makes the safe maneuver to not accelerate through the intersection – due to the elimination of reaction time, the T-bone collision is averted.

When humans drive, to subconsciously process the plethora of visual data surrounding them, they use billions of neurons to produce a data-center level of compute while consuming minimal power. To mitigate T-bone collisions, AVs must do even better, highlighting the biggest barrier to vehicle autonomy – the visual perception problem.

To solve this, an AV needs a platform with 75 tera-operations-per-second (TOPS) of compute for every Watt of power consumption. This solution must be robust against all exacerbating factors to flawlessly drive under any circumstance. Essentially, this solution must eliminate any need for human intervention. Through this, the non-zero reaction time is removed and the consequences of T-bone collisions can be alleviated.

To learn more about Recogni, check out www.recogni.com

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