Optimization-based Motion Planning

Smooth, stable and fast motion planning based on optimization-based approaches. (From Sept 2021 to Jun 2023)

Integrated Decision-making and Motion Planning to Enhance Oscilation-free Capability

Motivation

Unstable and Unsmooth motion in Uncertainty Environment:

  • Since there is a high-dimensional state/action modeling and the solution process considers the motion evolution of others, both POMDP and game-theory are easy to fall into the dimensionality problem, which makes the algorithm difficult to solve.
  • Most existing studies consider decision making and planning/control separately, simple decision results may not be effectively utilized by planning, which tends to make the solution process of motion planning time-consuming, or the planning unable to reach decision expectations, trajectory shaking and even solving failure in dealing with complex scenarios

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An integrated framework of decision-making and motion planning for autonomous driving focus on the lane change/keeping maneuvers. Firstly, we design a belief POMDP decision planner while building the uncertainty of the prediction trajectories. Through the Multi-policy forward solution, getting the optimal decision action and the corresponding future states with the consideration of the uncertainty risk for surrounding vehicles. Then, based on the decision results of lateral semantic behavior and longitudinal continuous acceleration, we build drivable corridors including the reference state and the related boundary constraints, which provide better suited information for planning to solve the optimal motion sequence more quickly and stably, and improve its consistency with decision module. Finally, we consider the vehicle dynamics and introduce some soft constraints to solve the optimal motion trajectory. png

Highlights

  • Able to make oscillation-free behavior decisions given biased prediction.
  • Able to cut through in the traffic efficiently and safely when being in squeezed.
  • Able to accelerate computation efficiency by building a state transfer model based on prediction uncertainty
  • ble to reduce the dissonance between decision-making and motion planning.

Some Reults

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Able to recat to deceleration in advanced in cut-in scenario with smoother acceleration and speed changes. In overtaking scenario, it able to overtake continuously with smoother lane-change driving way. It is also capable of avoiding possible lane-change failures and potential risks associated with reckless lane-change maneuver through uncertainty characterization.

Simulation demo in VTD: gif

Real Test with our autonomous electric vehicle platform: png gif png

Mixed Integer Programming with Hybrid Model Predictive Control

Collaborating student: Encheng Tu, Graduated Student.

Motivation

  • Try to directly integrate the semantic behavior decision, trajectroy planning and motion control into a single optimal problem.

An integrated motion planning scheme for autonomous vehicles, which incorporates integer lane state and its logical constraints into the Model Predictive Control (MPC) framework, forming a hybrid MPC-based motion planning framework.

Highlights

  • Motion planning without semantic decisions: a hybrid model predictive control (HMPC)-based seamless motion planner.
  • Able to integrate with external semantic decisions to achieve comprehensive optimization based on the external decisions.

Some Results

It allows for more efficient lane changing maneuvers in some scenarios wiith more rational lane-changing timing. png

Trajectory Planning and Tracking Control Based on Hierarchical MPC

A unified framework of trajectory planning and tracking control for autonomous overtaking, which is formed by hierarchical model predictive control, optimizing the lateral and longitudinal movement in two successive steps.

Highlights

  • A unified trajectory planning and tracking control framework for autonomous overtaking using hierarchical MPC.
  • Safety corridor generation on ST-Graph with different behavior mode.

Some Reults

It is able to perform smoothly driving maneuvers such as cornering and overtaking in CARLA simulation. png png

Publications:

    • Bo Leng, Ran Yu, Zhuoren Li*, et.al, “Seamless Overtaking Maneuvers for Automated Driving: Integrated Motion Planning Based on Hybrid Model Predictive Control,” IEEE Trans. Ind. Electron. (under review)
  2. Bo Leng, Lu Xiong, Zhuoren Li*, et.al. “Multi-mode Evasion Assistance Control Method for Intelligent Distributed-drive Electric Vehicle Considering Human Driver’s Reaction,” Chin. J. Mech. Eng. 2025, 38: 102. PDF, DOI.
  3. Zhuoren Li, Jia Hu, Bo Leng, Lu Xiong and Zhiqiang Fu, “An Integrated of Decision Making and Motion Planning Framework for Enhanced Oscillation-Free Capability,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 6, pp. 5718-5732, June 2024. PDF, DOI.
  4. Chengen Tu, Zhuoren Li, Bo Leng and Lu Xiong, “A Seamless Motion Planning Integrating Maneuver Decision Based on Hybrid Model Predictive Control,” in Proc. IEEE Int. Intell. Transp. Syst., 2023.
  5. Zhuoren Li, Lu Xiong Bo Leng, “A Unified Trajectory Planning and Tracking Control Framework for Autonomous Overtaking Based on Hierarchical MPC,” in Proc. IEEE Int. Intell. Transp. Syst., 2022, pp. 937-944.