benchflow-ai / vehicle-dynamics
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/vehicle-dynamics && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/4407" && unzip -o skill.zip -d .claude/skills/vehicle-dynamics && rm skill.zipProject Skills
This skill will be saved in .claude/skills/vehicle-dynamics/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.
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Skill Content
---
name: vehicle-dynamics
description: Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.
---
# Vehicle Dynamics Simulation
## Basic Kinematic Model
For vehicle simulations, use discrete-time kinematic equations.
**Speed Update:**
```python
new_speed = current_speed + acceleration * dt
new_speed = max(0, new_speed) # Speed cannot be negative
```
**Position Update:**
```python
new_position = current_position + speed * dt
```
**Distance Between Vehicles:**
```python
# When following another vehicle
relative_speed = ego_speed - lead_speed
new_distance = current_distance - relative_speed * dt
```
## Safe Following Distance
The time headway model calculates safe following distance:
```python
def safe_following_distance(speed, time_headway, min_distance):
"""
Calculate safe distance based on current speed.
Args:
speed: Current vehicle speed (m/s)
time_headway: Time gap to maintain (seconds)
min_distance: Minimum distance at standstill (meters)
"""
return speed * time_headway + min_distance
```
## Time-to-Collision (TTC)
TTC estimates time until collision at current velocities:
```python
def time_to_collision(distance, ego_speed, lead_speed):
"""
Calculate time to collision.
Returns None if not approaching (ego slower than lead).
"""
relative_speed = ego_speed - lead_speed
if relative_speed <= 0:
return None # Not approaching
return distance / relative_speed
```
## Acceleration Limits
Real vehicles have physical constraints:
```python
def clamp_acceleration(accel, max_accel, max_decel):
"""Constrain acceleration to physical limits."""
return max(max_decel, min(accel, max_accel))
```
## State Machine Pattern
Vehicle control often uses mode-based logic:
```python
def determine_mode(lead_present, ttc, ttc_threshold):
"""
Determine operating mode based on conditions.
Returns one of: 'cruise', 'follow', 'emergency'
"""
if not lead_present:
return 'cruise'
if ttc is not None and ttc < ttc_threshold:
return 'emergency'
return 'follow'
```