1. Situational Planning for the MIT DARPA Challenge Vehicle Thomas Coffee Image Credit: David Moore et al.

2. Original Problem Statement Inputs Vehicle path: position-space waypoint sequence ← Mission Planner Vehicle state: state-space configuration ← Perceptual State Estimator Obstruction environment: position-space regions, current velocities, and types (lanes, static obstacles, vehicles, unknowns) ← Perceptual State Estimator Law constraints: lane corridors and speed limits ← RNDF [ Sensor model … ← ??? ] Output (~10 Hz) Status: reports success/failure of each constraint on vehicle trajectory plan Vehicle trajectory: high-resolution state-space curve –Open-space-realizable by vehicle control system –Avoids input obstacles’ current-velocity-bounded subspace –Avoids input unknown regions’ zero-velocity subspace –Consistent with law constraints –Prioritizes inconsistent constraints by type: static > vehicle > unknown > lane > law –[ Attempts to achieve sensor coverage of unknown regions ] Time estimate to first waypoint in sequence

3. Revised Problem Statement Inputs Vehicle path: position-space waypoint sequence ← Mission Planner Vehicle state: state-space configuration ← Perceptual State Estimator Obstruction environment: position-space regions, current velocities, and types (lanes, static obstacles, vehicles, unknowns) ← Perceptual State Estimator Law constraints: lane corridors and speed limits ← RNDF [ Sensor model … ← ??? ] Output (~10 Hz) Status: reports success/failure of each constraint on vehicle trajectory plan Vehicle trajectory: high-resolution state-space curve –Open-space-realizable by vehicle control system –Avoids input obstacles’ current-velocity-bounded subspace –Avoids input unknown regions’ zero-velocity subspace –Consistent with law constraints –Prioritizes inconsistent constraints by type: static > vehicle > unknown > lane > law –[ Attempts to achieve sensor coverage of unknown regions ] Time estimate to first waypoint in sequence

4. Baseline Approach Deterministic (single) tree exploration in unobstructed configuration spacetime Tree maintenance/expansion based on D* Heuristic using modified Reeds-Shepp metric Node expansion using “hard” and “level” steer, accelerator/brake for optimality Reverse gear dynamics included by default Moderate aggressiveness: model dynamic obstacles as bounded by current velocity

5. Tree Node Expansion Design considerations: physical realizability optimality in path planning step computing cost plan computing cost complexity of implementation Project design: fixed constant lateral and tangential accelerations (2-D) time-quantized steps quantized velocity values constant-velocity step geometry precomputed grid conversions constant velocity only at max speed correct approximations through calculated control margins

6. Dynamic Updating Design considerations: information utilization obstacle update speed plan waiting time Project design: purge dynamically isolated branches from path tree handle obstacle detection through arc cost updates (+  ) in D* same mechanism may allow corrections if control margins exceeded in path execution current backpointer values in D* provide fallback if search exceeds desired cycle time

7. Dynamic Obstacles Design considerations: safety and aggression optimality of paths fear of the unknown Project design: consider all moving obstacles to have constant size and velocity (rely partially on other obstacles’ avoidance behavior) consider no dynamic obstacle threats from unobserved regions (rely on speed limits and sensors to provide sufficient warning) Note: no PSE routines currently available matching dynamic obstacle input specifications; hence may descope testing

8. Revised Project Plan Implementation & Testing Software testing with simulated splinter and vehicle dynamics (Dec 06) Hardware testing on splinter with added dynamic constraint layer (Dec 13) Hardware testing on DGC vehicle? Success Criteria Consistent path generation meeting constraints for reasonable driving environments Behavioral appropriateness of paths generated Sufficient plan frequency to maintain intended course and avoid dynamic obstacles in non-emergency scenarios