Lane Control OOD: Multi-Lane Traffic Management System
Question Details
Problem
Design a LaneController for a highway with n lanes. Vehicles enter and exit lanes. Each lane has a capacity. Support queries for the lane with the most available space, merging two adjacent lanes, and reporting congestion alerts when any lane exceeds 80% capacity.
python
class LaneController:
def __init__(self, n: int, capacity_per_lane: int): ...
def vehicle_enter(self, lane: int) -> bool:
"""Add vehicle to lane.
**Return** False if at capacity."""
...
def vehicle_exit(self, lane: int) -> bool:
"""Remove vehicle from lane.
**Return** False if lane is empty."""
...
def most_available_lane(self) -> int:
"""Return lane index with the most free space."""
...
def merge_lanes(self, lane_a: int, lane_b: int) -> None:
"""Merge two adjacent lanes into one with combined capacity."""
...
def get_congestion_alerts(self) -> list[int]:
"""Return list of lane indices exceeding 80% capacity."""
...
lc = LaneController(3, 10)
lc.vehicle_enter(0) # 7 times
lc.get_congestion_alerts() -> [0] # 7/10 = 70%? No, 8/10=80% triggers.
Follow-ups
- How does a priority queue help with
most_available_laneif lanes update frequently? - How do you handle
merge_laneswhen vehicles must be redistributed? - How would you make this thread-safe for concurrent highway simulations?
- Extend to support per-vehicle-type restrictions (e.g., lane 0 for trucks only).
Full Details
Problem
Design a LaneController for a highway with n lanes. Vehicles enter and exit lanes. Each lane has a capacity. Support queries for the lane with the most available space, merging two adjacent lanes, and reporting congestion alerts when any lane exceeds 80% capacity.
python
class LaneController:
def __init__(self, n: int, capacity_per_lane: int): ...
def vehicle_enter(self, lane: int) -> bool:
"""Add vehicle to lane.
**Return** False if at capacity."""
...
def vehicle_exit(self, lane: int) -> bool:
"""Remove vehicle from lane.
**Return** False if lane is empty."""
...
def most_available_lane(self) -> int:
"""Return lane index with the most free space."""
...
def merge_lanes(self, lane_a: int, lane_b: int) -> None:
"""Merge two adjacent lanes into one with combined capacity."""
...
def get_congestion_alerts(self) -> list[int]:
"""Return list of lane indices exceeding 80% capacity."""
...
lc = LaneController(3, 10)
lc.vehicle_enter(0) # 7 times
lc.get_congestion_alerts() -> [0] # 7/10 = 70%? No, 8/10=80% triggers.
Follow-ups
- How does a priority queue help with
most_available_laneif lanes update frequently? - How do you handle
merge_laneswhen vehicles must be redistributed? - How would you make this thread-safe for concurrent highway simulations?
- Extend to support per-vehicle-type restrictions (e.g., lane 0 for trucks only).
About This Question
This is a reported interview question from a zoox interview during the phone round.
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How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Zoox reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Zoox Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Zoox reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.