Install
$ agentstack add skill-xuansenpa1-skillrevise-vehicle-dynamics ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
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.
- Author: xuansenpa1
- Source: xuansenpa1/skillrevise
- License: MIT
- Homepage: https://arxiv.org/abs/2606.01139
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.