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Driving Lane Change
This is a behavior where the Ego Vehicle moves out of its Active (current) Driving Lane into an adjacent target Driving Lane. Ideally, the Ego Vehicle waits for space to open so that it can enter the target lane, indicates via turn signal in advance, enters the lane if it is still open (space available and traversal legal), continues to indicate and then waits a bit before any subsequent lane change.
A lot can go wrong with this ideal behavioral pattern. The driver may not signal adequately in advance, or a sudden intrusion into the target lane may force a return to the source lane. Nonetheless, we can anticipate and characterize both the ideal and non-ideal occurrences and stages in a Driving Lane Change.
Like all behaviors it terminates at some point successfully or unsuccessfully. An unsuccessful termination will be due to an undesired event or a timeout.
- Ego
- Target lane inside division + Target lane outside division + Road segment
Whether or not there is space available in the target lane to permit entry. This does not take into account whether or not it is currently legal to traverse into the target lane. So, while the target lane may be clear of ado vehicles, it may yet be illegal or physically impossible to cross over.
Type: Boolean
The target lane is clear of traffic and any dangerous obstacles, static or moving. This property applies to the target lane interior and not necessarily the interstitial Lane Division which may physically or legally forbid crossing.
(The external process that sets this value may consider the lane division, but we should not depend on that)
Type: Boolean
The target lane was entered before adequate time had passed. We set this value true to record the suboptimal condition while carrying on with the maneuver.
Type: Boolean
The time taken to cross over the interstitial Lane Division was exceeded during traversal into the target lane.
Type: Boolean
The overall time allotted for the Driving Lane Change maneuver was exceeded.
Type: Boolean
Copyright (c) 2023 Woven by Toyota, 2021,2022 Toyota Research Institute / MIT Open Source License