Technical Challenges in Planning and Control for Autonomous Heavy Trucks

Automatic driving of heavy trucks presents a highly complex system model, demanding real‑time, safe, and stable performance in high‑speed scenarios despite limited perception, localization, and computing resources. The core challenge is to plan and control the vehicle’s motion to satisfy safety, comfort, and fuel‑efficiency objectives.

Overview of Planning : In robotics, planning seeks an optimal path in configuration space; in AI, it seeks an optimal policy over state‑action pairs; in autonomous driving, planning and control together act as the vehicle’s brain and body, converting perception and localization into safe, comfortable motion.

Traditional Planning Framework :

The typical autonomous driving planner uses a hierarchical structure with a global layer (providing road‑level routing) and a local layer, which is further divided into a Behavioral Layer (high‑level decisions such as turn left/right, accelerate/decelerate) and Motion Planning (generating a state sequence for the vehicle to follow).

Behavioral Planning Methods :

Rule‑based method (Finite State Machine, FSM) – simple, effective, and explicit, defining states and transition conditions to handle complex environments.

POMDP – incorporates uncertainty by maintaining a belief over states and updating it with observations; solves for an optimal policy but is computationally intensive.

Learning‑based methods – deep learning, deep Q‑learning, MCTS, etc., achieve superior performance but require massive data collection and sophisticated pipelines.

Traditional Motion Planning Methods :

1. Search‑based – transforms planning into a graph search (e.g., Dijkstra, A*) and yields globally optimal solutions for low‑dimensional problems.

2. Sample‑based – samples the high‑dimensional state space (RRT, PRM) and connects samples with cost; provides probabilistic optimality but may be coarse under time constraints.

3. Optimization‑based – represents trajectories with analytical curves (polynomial, Bézier, spline) and solves a constrained optimization (NLP, QP) to satisfy multiple objectives.

Challenges Specific to Autonomous Heavy Trucks :

Safety – achieving reliability far beyond human drivers (e.g., >164,000 miles per incident).

Efficiency – matching human driver productivity.

Fuel economy – surpassing average human driver fuel consumption.

Complex dynamics – heavier inertia, multi‑system modeling (powertrain, brakes, steering, tires) leading to slower response.

Trailer coupling – introduces additional unstable modes, hinge dynamics, and load‑dependent behavior.

Load variations affect acceleration step‑response curves, making braking and control more demanding.

Economic driving strategies (e.g., using road grade information, anticipatory speed adjustments) can reduce fuel consumption by ~15% but may introduce safety trade‑offs.

Practical Attempts :

1. Autonomous lane‑change – overtaking a slower vehicle and returning to the original lane.

2. Merging – proactively yielding to vehicles entering from the left.

3. Trailer‑aware planning – incorporating the trailer’s geometry into the prediction and trajectory selection, similar to Model Predictive Control (MPC), to avoid obstacles while respecting the trailer’s swing.

These experiments demonstrate the additional considerations required when a heavy truck with a trailer navigates ramps and tight turns.

Overall, the talk highlighted the high‑level planning and control challenges for autonomous heavy trucks and presented several state‑of‑the‑art methods and experimental results.