Correction: Sarmadi et al. Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers. Sensors 2022, 22, 2301

Error in Algorithms 1 and 2

Algorithms 1 and 2 have been updated, with the new, correct versions shown below. The lines that needed to be corrected from the original paper [1] are annotated with dashed-line boxes. In the boxes below, you can see the corrections.

Algorithm 1 Finding the set of paths before stopping at red traffic lights during a day for a vehicle
Procedure FindPaths(C, S, T)
SP ← { i | si ∈ S ∧ si < 1 }	// All point indices with slow vehicle speed
P←∅	// P: The set of extracted traffic light paths
For i ∈ SP
If ∄ (R, D) ∈ P such that i ∈ R
t ← T(ci)	// t: coordinates of the closest traffic light
j ← i + 1
While j ≤ N ∧ (dist(cj, t) < dist(cj−1, t) ∧ dist(cj, t) < 50)	// Distance unit is meters
j ← j + 1
End While
If dist(cj₋1, t) < 5	//Distance unit is meters
While j > 1 ∧ (j−1 ∈ SP ∨ j ∉ SP)	//Go back to the last slowdown point
j ← j − 1
End While
P ← P ∪ CreatePath(C, j, t, P)
Break
End If
End If
End For
Return P
End Procedure

Algorithm 2 Creating a path given its closest point to the traffic light
Procedure CreatePath(C, j, t, P)
k ← 1
dk ← 0	// dk: Traversed distance to the stopping point at the path’s k-th index
rk ← j	// rk: The data point index at the path’s k-th index
While dk < Lp ∧ rk > 1 ∧ [∄ (R, D) ∈ P such that rk−1 ∈ R]
k ← k + 1
rk ← rk−1 − 1
dk ← dk−1 + dist(crk, crk−1)
End While
R ← ⟨r1, r2, ..., rk⟩
D ← ⟨d1, d2, ..., dk⟩
If dk ≥ Lp	// If the path’s traversed distance is greater than Lp
Return {(R, D)}
Else
Return ∅
End If
End Procedure

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.