Collision Avoidance Safety Filter for Autonomous E-Scooter Using Ultrasonic Sensors

Core Concepts

Proposing a collision avoidance safety filter for autonomous e-scooters using low-cost ultrasonic sensors to prevent collisions with obstacles and ensure safe operation in pedestrian areas.

Abstract

The content discusses the development of a collision avoidance safety filter for autonomous e-scooters using ultrasonic sensors. It covers the challenges faced by shared e-scooter systems, the need for autonomous e-scooters, and the proposed controller structure. Real-world experiments demonstrate the effectiveness of the safety filter in avoiding collisions with obstacles. The hardware setup, system dynamics, and distance measurement filtering are detailed. The controller design ensures safe operation by adjusting velocity based on detected obstacles.

Introduction to shared e-scooter challenges.
Proposal for an autonomous e-scooter with a safety filter.
Controller structure overview and real-world experiments.
Hardware setup, system dynamics, and distance filtering.
Detailed explanation of the controller design.

Stats

Based on possibly faulty distance measurements, we design a filter to mitigate measurement noise and missing values as well as a gain-scheduled controller to limit the velocity commanded to the e-scooter when required due to imminent collisions.
The proposed approach is designed such that it may be easily deployed in similar applications of general micromobility vehicles.

Quotes

"The proposed controller structure is a simple and intuitive way to ensure collision avoidance for autonomous vehicles relying only on low-cost ultrasonic sensors."
"The collision avoidance safety filter allows the e-scooter to safely navigate in an environment with unknown obstacles."

Key Insights Distilled From

Collision Avoidance Safety Filter for an Autonomous E-Scooter using Ultrasonic Sensors

by Robi... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15116.pdf

Deeper Inquiries

How can additional perception sensors enhance obstacle detection in future iterations?

In future iterations, incorporating additional perception sensors, such as cameras, can significantly enhance obstacle detection capabilities. Cameras provide visual data that can be processed using computer vision algorithms to identify and classify obstacles more accurately. This allows for the recognition of complex objects, patterns, and movements that may not be easily detected by ultrasonic sensors alone. By combining data from multiple sensor types, including cameras, lidar, radar, or infrared sensors, the system can create a more comprehensive and detailed understanding of the surrounding environment. This multi-sensor fusion approach improves reliability and robustness in detecting obstacles under various conditions like low light or adverse weather.

What are potential drawbacks or limitations of relying solely on low-cost ultrasonic sensors for collision avoidance?

While low-cost ultrasonic sensors offer affordability and simplicity in implementation for collision avoidance systems in micromobility vehicles like e-scooters, they come with certain drawbacks and limitations. One major limitation is their range capability; ultrasonic sensors have limited range compared to other sensor technologies like lidar or radar. This restricted range may result in late detections of fast-moving obstacles or insufficient time to react appropriately.
Another drawback is their susceptibility to environmental factors such as temperature changes or acoustic interferences which could lead to inaccurate readings. Additionally, ultrasonic waves might reflect off surfaces at odd angles causing false positives or negatives in obstacle detection.
Moreover, the field of view provided by ultrasonic sensors is typically narrow compared to other sensor types like cameras which offer a broader perspective. This limited field of view could result in blind spots where obstacles go undetected leading to potential collisions.

How might advancements in computer vision technology impact the effectiveness of this collision avoidance safety filter?

Advancements in computer vision technology have the potential to greatly enhance the effectiveness of this collision avoidance safety filter by providing more sophisticated object recognition capabilities based on visual input from cameras. Computer vision algorithms can analyze images captured by onboard cameras to detect not only static but also dynamic objects with greater accuracy than traditional proximity-based sensing methods like ultrasonics.
By leveraging deep learning techniques within computer vision systems, it becomes possible to train models that can recognize a wide variety of objects including pedestrians, cyclists, vehicles - even predicting their trajectories based on movement patterns observed over time.
Furthermore,
computer vision enables semantic segmentation allowing for better understanding
of different elements within an image - distinguishing between road signs,
pedestrians crossing streets etc., thereby enhancing situational awareness.
This level
of detail aids decision-making processes related
to navigation paths around identified obstacles.
Overall,
advancements
in computer vision technology would enable more precise identification,
tracking,
and prediction
of moving entities within an autonomous vehicle's environment resulting
in improved safety measures through proactive collision avoidance strategies.