---
title: "Bouton 2018"
type: concept
related: [Sarsop, Utility Fusion, Qmdp, Partially Observable Markov Decision Process]
source: https://www.jemoka.com/posts/kbhbouton_2018/
confidence: high
status: active
---

(Bouton et al. 2018)
One-Liner Uses the single-user avoidance POMDP formulation presented in (Bouton, Cosgun, and Kochenderfer 2017) to extend to multiple road users
Novelty Uses Single-User Model of Road Navigation to extend general POMDP formulation into multi-pedestrian/multi road user casesroad user cases
Previous Work Imagine worst-case scenario always: set upper bound and always imagine it; could cause gridlock if situation never resolves.
Notable Methods Uses QMDP and SARSOP to perform optimization
Single-User Model of Road Navigation See Single-User Model of Road Navigation
Scaling to multiple road users make an aggregate utility which is a function across all the single-user avoidance strategies (i.e. the aggregate utiltiy of mulitlpe road user is the utility of avoiding each individual user) \(U^{*}(b,a) = f(U^{*}(b_1, a) &hellip; U^{*}(b_{n}, a)\). this is called utility fusion two possible approaches: either minimum of all the utilities, or the sum of them; the former is more risk averse (we want to hit no one), and latter treats each user is independent. further, the number of users in the road is modeled by a belief Evaluation &ldquo;the evaluation models are different to find the optimal policy, and are also higher fidelity&rdquo;
We want to evaluate our POMDP on a higher fidelity model to check if the system can generalize to harder environments.
Baselines: random actions, or hand crafted rules-based policy.
Key Figs New Concepts Single-User Model of Road Navigation POMDP formulation; we only care about one road user
action: a finite set of change in acceleration -4m/s2, -2m/s2, 0m/s2, 2m/s2, 4m/s2 states and transitions: poses (position + velocity) of the car and the road user; position are velocities are discretized observation: measured position and velocity of the one other road user with a pm 1 meter variance for crosswalks and pm 2 meter variance for intersection users in non-occluded area will always be detected user in an occluded area will not be detected position and velocity of road users are uncertain pm 1 meter and pm 1 meter / second belief: categorical distribution over states dynamics: physics + kinematics for car; pedestrians have stochastic velocity reward: unit reward for final position, tuned penalty for collision Notes
