There are many algorithms for constructing a route (Voronoi algorithm, A*, D*, Wave front, RRT, PRM, etc.). All route construction algorithms are executed at different speeds. At the same time, the speed of execution of the algorithm is closely related to the surrounding space: whether the passability map is known, the number of objects on the map, whether there are moving objects, etc.
I see the following conclusions:
1) To ensure the safety of the autonomous vehicle, it is necessary to have:
- fast algorithms for constructing a global route;
- fast algorithms for rebuilding a local route to avoid obstacles;
- fast algorithms for integrating data from different sensors;
- high-speed computers on which the route construction algorithms will be processed.
2) Several route construction algorithms must be implemented in an autonomous vehicle. The control system of an autonomous vehicle must select the necessary route construction algorithm, depending on external situations. The control system of an autonomous vehicle must be trained to select the optimal algorithm for constructing a route.
All known route-building algorithms must first be tested on various maps and in various driving scenarios.
🚘 1. Real-Time Obstacle Avoidance
- How it helps: Continuously plans around pedestrians, other vehicles, cyclists, or unexpected obstacles (e.g., a fallen branch).
- Techniques: Local path planners like Dynamic Window Approach (DWA) or Model Predictive Control (MPC) dynamically adjust the vehicle's trajectory. Fusion of sensor data (LiDAR, radar, camera) to detect and track obstacles.
🧠 2. Predictive Path Planning
- How it helps: Anticipates potential movements of surrounding agents and adjusts path proactively.
- Example: Predicts that a pedestrian might jaywalk, or that another car might change lanes without signaling.
- Techniques: Trajectory prediction models using AI or probabilistic models (e.g., Kalman filters, Bayesian networks).
🛑 3. Emergency Maneuver Planning
- How it helps: Quickly re-plans a safe evasive path in case of sudden danger (e.g., a vehicle braking hard ahead).
- Techniques: Pre-computed safety corridors and real-time re-planning allow for immediate alternate routes. Integration with vehicle dynamics to ensure physical feasibility of maneuvers.
🗺️ 4. Handling Complex Road Structures
- How it helps: Understands and navigates safely through roundabouts, merges, multi-lane roads, or temporary roadblocks.
- Techniques: HD maps + semantic understanding from cameras and LiDAR. Pathfinding algorithms integrate lane-level navigation, rules (e.g., right-of-way), and dynamic context (e.g., construction zones).
🚦 5. Decision-Making at Intersections and Traffic Lights
- How it helps: Makes safe and legal decisions (e.g., when to yield or go) based on dynamic agents and signals.
- Techniques: Combines behavior prediction, rule-based logic, and motion planning to create smooth, legal, and safe paths.
🧍 6. Pedestrian Awareness and Safety Margins
- How it helps: Maintains a safe distance and reacts cautiously around unpredictable pedestrians, especially in urban areas.
- Techniques: Inflate cost maps near pedestrians. Use behavior-aware path planning to avoid sudden stops or risky overtakes.
🔄 7. Continuous Re-Planning
- How it helps: Updates the planned path in real time, ensuring responsiveness to any environmental or behavioral change.
- Techniques: Receding Horizon Planning and sliding window techniques to adapt continuously without full recomputation.
🔧 Summary: How Pathfinding Enhances Safety
FeatureBenefitObstacle AvoidancePrevents collisions in real-timePredictive PlanningAnticipates risk before it occursEmergency HandlingEnables fast, safe evasive actionsComplex NavigationHandles urban chaos confidentlySignal/Rule ComplianceEnsures lawful and safe movementPedestrian AwarenessEnhances human-vehicle interaction safetyRe-PlanningKeeps up with dynamic environments