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Insect-inspired night-time collision detector based on atomically thin and light-sensitive memtransistors

Despite only about 25% of car travel happening after dark, almost half of fatal accidents occur at night. As vehicles become more advanced and even autonomous, the ways of detecting and avoiding these collisions must evolve too. Current systems are often complicated, resource-intensive or work poorly in the dark. But now, researchers from Penn State University reporting in an open-access paper in ACS Nano have designed a simple, power-saving collision detector inspired by the way insects avoid bumping into one another.

Numerous collision avoidance systems (CASs) are already included in vehicles, and they can automatically brake when an object gets too close. Some operate by analyzing an image of the space around the car, but in conditions such as heavy rain or low light, the image isn’t as clear. To make up for it, complicated signal processors are used to make sense of what is still visible.

Another method is to incorporate either radar or lidar (light detection and ranging) sensors, but these are difficult to miniaturize and need a lot of power. In the end, these instruments can add unnecessary weight, energy requirements and complications, despite making the vehicle safer.

But insects, including locusts and flies, can easily avoid collisions with each other without relying on fancy software or lidar, even at night. Instead, they engage certain obstacle-avoiding neural circuits, which are highly efficient and could inspire a next-generation CAS. Corresponding author Saptarshi Das and colleagues wanted to create an insect-inspired collision detector adapted to sense vehicles that was effective, safe and consumed less power than its predecessors.

First, the team designed an algorithm based on the neural circuitry insects use to avoid an obstacle. Instead of processing an entire image, they only processed one variable: the intensity of a car’s headlights. Without the need for an onboard camera or image sensor, the detection and processing units were combined, making the overall detector smaller and more energy efficient.

The sensor was comprised of eight photosensitive “memtransistors” constructed from a layer of molybdenum disulfide (MoS2), organized onto a circuit. It took up only 40 µm2 and used only a few hundred picojoules of energy—tens of thousands of times less than existing systems.

In real-life, nighttime scenarios, the detector could sense a potential two-car accident two to three seconds before it happened, leaving the driver with enough time to take critical corrective action. The researchers say that this novel detector can help make existing CASs better and safer.

The authors acknowledge funding from the Army Research Office and the National Science Foundation.


  • Darsith Jayachandran, Andrew Pannone, Mayukh Das, Thomas F. Schranghamer, Dipanjan Sen, and Saptarshi Das (2023) “Insect-Inspired, Spike-Based, in-Sensor, and Night-Time Collision Detector Based on Atomically Thin and Light-Sensitive Memtransistors” ACS Nano doi: 10.1021/acsnano.2c07877



Some thoughts on this:
A copying insects is a good idea as they are pretty good at obstacle avoidance with very little hardware and low power consumption. This is mainly due to using an analog sensor based approach rather than a digital image based one.

However, it will only work for night time, lights on crashes. It won't alarm for a Moose or a human wheeling a bike, or in daylight.

Nonetheless, it is a good approach compared to the image processing based "meatgrinder" approach.

You might also suggest that insects are not good at dodging cars when you look at the front of your car after a long run, but they did not evolve to avoid huge objects moving at 100 kph.

Also, the number of insects being killed "per mile traveled" has declined drastically over the last few decades:
Which probably isn't good.

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