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Vehicle Dynamics

skill-xuansenpa1-skillrevise-vehicle-dynamics · by xuansenpa1

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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Install

$ agentstack add skill-xuansenpa1-skillrevise-vehicle-dynamics

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Security review

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No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Vehicle Dynamics Simulation

Basic Kinematic Model

For vehicle simulations, use discrete-time kinematic equations.

Speed Update:

new_speed = current_speed + acceleration * dt
new_speed = max(0, new_speed)  # Speed cannot be negative

Position Update:

new_position = current_position + speed * dt

Distance Between Vehicles:

# 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:

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:

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:

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:

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'

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.