The autonomous vehicle system would likely utilize a combination of machine learning and classical control algorithms to enable safe navigation and control of the vehicle without human input. At a high level, machine learning algorithms like neural networks would be used for perception, prediction, and planning tasks, while classical controls approaches would handle lower level actuation and motion control.

For perception, deep convolutional neural networks (CNNs) are well-suited for computer vision tasks like object detection, classification, and semantic segmentation using camera and LiDAR sensor data. CNNs can be trained on huge datasets of manually labeled sensor data to learn visual features and detect other vehicles, pedestrians, road markings, traffic signs, and other aspects of the driving environment. Similarly, recurrent neural networks (RNNs) like LSTMs are well-optimized for temporal sequence prediction using inputs like past vehicle trajectories, enabling the prediction of other road users’ future motions.

Higher level path planning and decision making tasks could leverage techniques like model predictive control (MPC) integrated with neural network policies. An MPC framework would optimize a cost function over a finite time horizon to generate trajectory, velocity, and control commands while satisfying constraints. The cost function could include terms for safety objectives like collision avoidance while also optimizing for ride quality. Constraints would ensure kinematic and dynamic feasibility of the planned motion. Additionally, imitation learning or reinforcement learning could train a neural network policy to map directly from perceptual inputs to motion plans by mimicking demonstrations from human drivers or via trial-and-error experience in a simulator.

Low level controller tasks would require precise, real-time control of acceleration, braking, and steering actuators. Proportional-integral-derivative (PID) controllers are well-suited for this application given their simplicity, robustness, and ability to systematically stabilize around a target trajectory or other reference signals. Separate PID controllers could actuate individual control surfaces like throttle, brake, and steering to regulate longitudinal speed tracking and lateral path following errors according to commands from higher level planners. Gains for each PID controller would need tuning to provide responsive yet stable control without overshoot or oscillation.

Additional control techniques like linear quadratic regulation (LQR) could also be applied for trajectory tracking tasks. LQR is an optimal control method that provides state feedback gains to optimize a linearized system about an equilibrium or nominal operating point. It can systematically achieve stable, high-performance regulation for both longitudinally and laterally by balancing control effort with tracking errors. LQR gains could also be scheduled as a function of vehicle velocity to achieve improved handling dynamics across different operating regimes.

Coordinated control of both lateral and longitudinal motion would require an integrated framework. Kinematic and dynamic vehicle models relating acceleration, velocity, steering angle, yaw rate, and lateral position could be linearized around an operating point. This generates a linear time-invariant system amenable to analysis using well-established multi-input multi-output (MIMO) control design techniques like linear matrix inequalities (LMIs). MIMO control achieves fully coupled, optimally coordinated actuation of all control surfaces for robust stability and handling qualities.

Fault tolerance, safety, and redundancy are also crucial considerations. Control systems should systematically identify sensor failures or abnormalities and gracefully degrade functionality. Architectures like control allocations could address actuator faults by redistributing commands across healthy effectors. Fail-safe actions like slow, steady stops should be triggered if critical hazards cannot be avoided. Control systems could operate on simple kinematic approximations as a fallback if more sophisticated dynamic models become unreliable.

An intelligent combination of machine learning, optimal control, classical control, and robust/fault-tolerant techniques offers a rigorous and trustworthy approach for autonomously navigating roadways without direct human intervention. Careful system integration and verification/validation efforts would then be required to safely deploy such capabilities on public roads around humans on a large scale.