SkillCompiler/data/skills-bench/tasks/adaptive-cruise-control/task.md

schema_version, metadata, verifier, agent, environment

schema_version	metadata	verifier	agent	environment
1.3	author_name author_email difficulty category subcategory category_confidence task_type modality interface skill_type tags id name Jiachen Li jiachenli@utexas.edu medium industrial-physical-systems vehicle-control high implementation simulation csv source-code time-series terminal python domain-procedure mathematical-method python pid-control simulation vehicle-dynamics automotive control-theory adaptive-cruise-control Adaptive Cruise Control Simulation	type timeout_sec service hardening test-script 1800.0 main cleanup_conftests true	timeout_sec 1800.0	network_mode build_timeout_sec os cpus memory_mb storage_mb gpus public 600.0 linux 1 2048 5120 0

You need to implement an Adaptive Cruise Control (ACC) simulation that maintains the set speed (30m/s) when no vehicles are detected ahead, and automatically adjusts speed to maintain a safe following distance when a vehicle is detected ahead. The targets are: speed rise time <10s, speed overshoot <5%, speed steady-state error <0.5 m/s, distance steady-state error <2m, minimum distance >5m, control duration 150s. Also consider the constraints: initial speed ~0 m/s, acceleration limits [-8.0, 3.0] m/s^2, time headway 1.5s, minimum gap 10.0m, emergency TTC threshold 3.0s, timestep 0.1s. Data is available in vehicle_params.yaml(Vehicle specs and ACC settings) and sensor_data.csv (1501 rows (t=0-150s) with columns: time, ego_speed, lead_speed, distance, collected from real-world driving).

First, create pid_controller.py to implement the PID controller. Then, create acc_system.py to implement the ACC system and simulation.py to run the vehicle simulation. Next, tune the PID parameters for speed and distance control, saving results in tuning_results.yaml. Finally, run 150s simulations, producing simulation_results.csv and acc_report.md.

Examples output format:

pid_controller.py: Class: PIDController Constructor: init(self, kp, ki, kd) Methods: reset(), compute(error, dt) returns float

acc_system.py: Class: AdaptiveCruiseControl Constructor: init(self, config) where config is nested dict from vehicle_params.yaml (e.g., config['acc_settings']['set_speed']) Method: compute(ego_speed, lead_speed, distance, dt) returns tuple (acceleration_cmd, mode, distance_error) Mode selection: 'cruise' when lead_speed is None, 'emergency' when TTC < threshold, 'follow' when lead vehicle present

simulation.py: Read PID gains from tuning_results.yaml file at runtime. Do not embed auto-tuning logic because gains should be loaded from the yaml file. Uses sensor_data.csv for lead vehicle data (lead_speed, distance).

tuning_results.yaml, kp in (0,10), ki in [0,5), kd in [0,5): pid_speed: kp: w ki: kd: pid_distance: kp: ki: kd:

simulation_results.csv: (exactly 1501 rows, exact same column order) time,ego_speed,acceleration_cmd,mode,distance_error,distance,ttc 0.0,0.0,3.0,cruise,,, 0.1,0.3,3.0,cruise,,, 0.2,0.6,3.0,cruise,,, 0.3,0.9,3.0,cruise,,, 0.4,1.2,3.0,cruise,,, 0.5,1.5,3.0,cruise,,, 0.6,1.8,3.0,cruise,,,

acc_report.md: Include sections covering: System design (ACC architecture, modes, safety features) PID tuning methodology and final gains Simulation results and performance metrics