Control, Planning, and Decision-Making
Autonomous vehicles do not become safe only by perceiving the world correctly. They must also decide what to do, plan a feasible motion, and execute that motion through the vehicle actuators. This chapter focuses on the part of the autonomy stack that transforms environmental understanding into safe action: decision-making, motion planning, and control. Perception and localisation estimate the state of the vehicle and its surroundings; prediction estimates how other actors may move; decision-making selects the intended maneuver; motion planning generates a feasible trajectory; control tracks that trajectory through steering, braking, throttle, thrust, or other actuators; and monitoring and fallback functions supervise the result and trigger replanning or minimal-risk behavior when needed.
These functions are strongly interdependent. A motion planner may generate a valid trajectory in isolation, but still fail at system level if perception is delayed, localisation drifts, prediction is uncertain, or the controller cannot physically track the planned path. Likewise, a controller may perform well against a clean reference trajectory, but still produce unsafe or uncomfortable behavior if the planner generates abrupt, infeasible, or poorly timed commands. For this reason, planning and control validation cannot be limited to unit testing of individual algorithms. Unit testing is necessary, but it must be connected to integration testing, system-level scenario testing, and operational validation inside the intended Operational Design Domain (ODD).
The goal of this chapter is therefore not only to introduce common control and planning methods, but to show how they are validated as part of a complete autonomous vehicle system. The chapter positions decision-making, motion planning, and control within the autonomy stack, then discusses the main methods and architectures used in these layers, including classical control, AI-based control, behavioural decision-making, and trajectory planning. The emphasis is placed on validation implications: what must be checked, what evidence is needed, and how failures may appear when the component is integrated into the full system.
A central idea in this chapter is that planning and control validation should be scenario-based. Autonomous vehicles operate in environments where safety depends on interactions between the ego vehicle, other road users, infrastructure, road geometry, environmental conditions, and vehicle dynamics. Therefore, it is not enough to ask whether a planner works for one example case. Instead, engineers must define scenario families, vary their parameters, measure system behavior, and evaluate whether the vehicle remains safe, legal, comfortable, and robust under the expected range of conditions.
The chapter also connects planning and control validation to the broader systems-engineering process. In the V-model perspective introduced earlier in the handbook, planning and control appear at several validation levels: component verification, integration testing, system validation, and operational validation. By the end of this chapter, the reader should understand how to move from algorithm descriptions to a validation workflow: define the function under test, identify its ODD and assumptions, design scenarios, select measurable performance and safety criteria, choose suitable test methods, and package the resulting evidence for safety assurance.
