LeetCode #1776 — HARD

Car Fleet II

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

There are n cars traveling at different speeds in the same direction along a one-lane road. You are given an array cars of length n, where cars[i] = [positioni, speedi] represents:

  • positioni is the distance between the ith car and the beginning of the road in meters. It is guaranteed that positioni < positioni+1.
  • speedi is the initial speed of the ith car in meters per second.

For simplicity, cars can be considered as points moving along the number line. Two cars collide when they occupy the same position. Once a car collides with another car, they unite and form a single car fleet. The cars in the formed fleet will have the same position and the same speed, which is the initial speed of the slowest car in the fleet.

Return an array answer, where answer[i] is the time, in seconds, at which the ith car collides with the next car, or -1 if the car does not collide with the next car. Answers within 10-5 of the actual answers are accepted.

Example 1:

Input: cars = [[1,2],[2,1],[4,3],[7,2]]
Output: [1.00000,-1.00000,3.00000,-1.00000]
Explanation: After exactly one second, the first car will collide with the second car, and form a car fleet with speed 1 m/s. After exactly 3 seconds, the third car will collide with the fourth car, and form a car fleet with speed 2 m/s.

Example 2:

Input: cars = [[3,4],[5,4],[6,3],[9,1]]
Output: [2.00000,1.00000,1.50000,-1.00000]

Constraints:

  • 1 <= cars.length <= 105
  • 1 <= positioni, speedi <= 106
  • positioni < positioni+1
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: There are n cars traveling at different speeds in the same direction along a one-lane road. You are given an array cars of length n, where cars[i] = [positioni, speedi] represents: positioni is the distance between the ith car and the beginning of the road in meters. It is guaranteed that positioni < positioni+1. speedi is the initial speed of the ith car in meters per second. For simplicity, cars can be considered as points moving along the number line. Two cars collide when they occupy the same position. Once a car collides with another car, they unite and form a single car fleet. The cars in the formed fleet will have the same position and the same speed, which is the initial speed of the slowest car in the fleet. Return an array answer, where answer[i] is the time, in seconds, at which the ith car collides with the next car, or -1 if the car does not collide with the next car.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Math · Stack

Example 1

[[1,2],[2,1],[4,3],[7,2]]

Example 2

[[3,4],[5,4],[6,3],[9,1]]

Related Problems

  • Car Fleet (car-fleet)
  • Count Collisions on a Road (count-collisions-on-a-road)
Step 02

Core Insight

What unlocks the optimal approach

  • We can simply ignore the merging of any car fleet, simply assume they cross each other. Now the aim is to find the first car to the right, which intersects with the current car before any other.
  • Assume we have already considered all cars to the right already, now the current car is to be considered. Let’s ignore all cars with speeds higher than the current car since the current car cannot intersect with those ones. Now, all cars to the right having speed strictly less than current car are to be considered. Now, for two cars c1 and c2 with positions p1 and p2 (p1 < p2) and speed s1 and s2 (s1 > s2), if c1 and c2 intersect before the current car and c2, then c1 can never be the first car of intersection for any car to the left of current car including current car. So we can remove that car from our consideration.
  • We can see that we can maintain candidate cars in this way using a stack, removing cars with speed greater than or equal to current car, and then removing cars which can never be first point of intersection. The first car after this process (if any) would be first point of intersection.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1776: Car Fleet II
class Solution {
    public double[] getCollisionTimes(int[][] cars) {
        int n = cars.length;
        double[] ans = new double[n];
        Arrays.fill(ans, -1.0);
        Deque<Integer> stk = new ArrayDeque<>();
        for (int i = n - 1; i >= 0; --i) {
            while (!stk.isEmpty()) {
                int j = stk.peek();
                if (cars[i][1] > cars[j][1]) {
                    double t = (cars[j][0] - cars[i][0]) * 1.0 / (cars[i][1] - cars[j][1]);
                    if (ans[j] < 0 || t <= ans[j]) {
                        ans[i] = t;
                        break;
                    }
                }
                stk.pop();
            }
            stk.push(i);
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n)
Space
O(n)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

For each element, scan left (or right) to find the next greater/smaller element. The inner scan can visit up to n elements per outer iteration, giving O(n²) total comparisons. No extra space needed beyond loop variables.

MONOTONIC STACK
O(n) time
O(n) space

Each element is pushed onto the stack at most once and popped at most once, giving 2n total operations = O(n). The stack itself holds at most n elements in the worst case. The key insight: amortized O(1) per element despite the inner while-loop.

Shortcut: Each element pushed once + popped once → O(n) amortized. The inner while-loop does not make it O(n²).
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.

Breaking monotonic invariant

Wrong move: Pushing without popping stale elements invalidates next-greater/next-smaller logic.

Usually fails on: Indices point to blocked elements and outputs shift.

Fix: Pop while invariant is violated before pushing current element.