## Disengagement probability estimation

Organisatsiooni nimi

Autonomous Driving Lab

Kokkuvõte

Testing of autonomous vehicles on public streets is performed with safety drivers. The task of a safety driver is to monitor the car behavior and take over the control if the car behaves erratically. This is called disengagement and all disengagements are meticulously logged and analyzed.

The task in this project is to estimate the probability of disengagement on a specific section of a road. From the individual disengagement probabilities of road sections a total disengagement probability for a certain route can be calculated. If the total probability of disengagement along a route is below a certain threshold, a driverless vehicle is dispatched. If not, then an autonomous vehicle with a safety driver is dispatched.

In theory calculating a disengagement probability for a road section is simple - just no_of_disengagements_in_this_section / no_of_times_the_vehicle_passed_this_section. But in practice the vehicle has passed this section maybe only a handful of times. The key idea is that we should group or cluster the road sections to increase the denominator.

The tasks:

* Together with supervisors come up with features or attributes how to group or cluster the road sections.

* Train a machine learning model (logistic regression) to predict probability of disengagement from those features.

* Validate the approach on Autonomous Driving Lab data and maps.

Links:

* https://www.rand.org/pubs/research_reports/RR1478.html

The task in this project is to estimate the probability of disengagement on a specific section of a road. From the individual disengagement probabilities of road sections a total disengagement probability for a certain route can be calculated. If the total probability of disengagement along a route is below a certain threshold, a driverless vehicle is dispatched. If not, then an autonomous vehicle with a safety driver is dispatched.

In theory calculating a disengagement probability for a road section is simple - just no_of_disengagements_in_this_section / no_of_times_the_vehicle_passed_this_section. But in practice the vehicle has passed this section maybe only a handful of times. The key idea is that we should group or cluster the road sections to increase the denominator.

The tasks:

* Together with supervisors come up with features or attributes how to group or cluster the road sections.

* Train a machine learning model (logistic regression) to predict probability of disengagement from those features.

* Validate the approach on Autonomous Driving Lab data and maps.

Links:

* https://www.rand.org/pubs/research_reports/RR1478.html

Lõputöö kaitsmise aasta

2024-2025

Juhendaja

Tambet Matiisen, Edgar Sepp, Meelis Kull

Suhtlemiskeel(ed)

eesti keel, inglise keel

Nõuded kandideerijale

Tase

Bakalaureus, Magister

Märksõnad

### Kandideerimise kontakt

Nimi

Tambet Matiisen

Tel

E-mail

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