VisionPilot 0.9

Key Features Added in v0.9

• Longitudinal Perception & Tracking

  • Integrated AutoSpeed detector and ObjectFinder multi-object tracker.
  • Robust CIPO selection (Level 1 & Level 2), Kalman-based distance and relative-speed, cut‑in detection, Kalman reset handling.

• Speed Planning (RSS-inspired)

  • New SpeedPlanner computes safe following distance d_min and ideal_speed_ms.
  • Encodes FCW/AEB zones, exposes fcw_active and aeb_active.

• Longitudinal Controller

  • New longitudinal PID controller tracks ideal_speed_ms and outputs control_effort_ms2 (accel/decel).

• Parallel Lateral + Longitudinal Pipelines

  • Shared capture buffer at configurable pipeline.target_fps.
  • All longitudinal tuning (AutoSpeed thresholds, PID gains, ego-speed fallback) moved into visionpilot.conf.

• Shared-Memory IPC

  • POSIX shm segment /visionpilot_state exposing a single VisionPilotState struct with:
    • Lateral outputs, longitudinal CIPO/safety metrics, control effort, and CAN/ego state.
  • tools/shm_reader utility to inspect outputs frame‑by‑frame.

System Architecture

Vision Pilot 0.9 processes images from a single front-facing camera to enable both ADAS features and highway autopilot within a single driving lane. Compared to Vision Pilot 0.9, this version of system incorporates both lateral perception and planning modules alongside longitudintal perception and planning modules in two parallel streams, through the additional integration of the AutoSpeed neural network for closest-in-path-object detection. This enables features such as Autonomous Cruise Control, Forward Collision Warning, and Automatic Emergency braking. In order to estimate the distance of the closest-in-path-object, a homography transform is utilized which maps image pixels to road coordinates, providing an estimate of real-world distances in metres. A Kalman filter is used to track the distance of the closest-in-path-object and estimate its velocity. To maintain a safe following distance to the lead vehicle, the system complies with Mobileye's Responsibility Sensitive Safety framework.

Contributions

@m-zain-khawaja along with the team @pranavreddy23, @atanasko , and @TranHuuNhatHuy