Senior Control Algorithm Engineer

该职位来源于猎聘 Job Responsibilities Responsible for the algorithm design, modeling, simulation iteration, and real-vehicle calibration and deployment of longitudinal and lateral vehicle control as well as trailer attitude control. Drive the full process from model development and simulation validation to real-vehicle debugging, supporting the stable operation and mass-production delivery of autonomous driving functions for heavy trucks in line-haul logistics, highway pilot, and low-speed yard scenarios.

Core responsibilities are as follows: Control Algorithm MBD Modeling and Development: Using Matlab/Simulink and Stateflow tools and following a standard MBD development process, complete the modeling, logic development, and algorithm packaging of core modules such as lateral steering control, longitudinal speed/torque control, car-following control, and path-tracking control for heavy-truck autonomous driving, ensuring models are standards-compliant, reusable, and code-generation ready.

Iterative

Optimization of Commercial-Vehicle-Specific Control Strategies: Addressing the unique characteristics of heavy trucks — large inertia, variable payload, body lag, trailer articulation, chassis nonlinearity, and high-speed shimmy — optimize adaptive control strategies for vehicle and trailer attitude, solving core issues such as steering lag of large vehicles, speed overshoot, trailer trajectory deviation, jackknifing, and control jitter on bumpy roads, and adapting to full-scenario operating conditions including high speed, long downhill grades, curves, reversing, and unstructured yards. Full-Process Simulation Validation and Adaptation: Independently complete MIL/SIL simulation modeling, scenario adaptation, and regression testing of control models; cooperate with the HIL hardware-in-the-loop bench to complete model joint debugging, signal adaptation, and fault-condition verification; and iteratively optimize the stability and robustness of control models to meet the simulation admission standards of the autonomous driving system. Real-Vehicle Calibration and Closed-Loop Issue Resolution: Responsible for real-vehicle control algorithm debugging, parameter calibration, and operating-condition adaptation for heavy trucks and trailers; follow up on vehicle and trailer control-related anomalies during road testing — including poor tracking accuracy, jerk on start/stop, steering oscillation, trailer shimmy, reversing trajectory deviation, and speed fluctuation — and complete issue localization, model modification, parameter iteration, and regression verification to form a complete closed loop.

Model Version and Standards Governance: Following automotive embedded software development standards and MBD modeling standards, complete model version management, baseline freezing, and change record archiving; support functional safety self-inspection; and deliver deliverables such as model design documents, test reports, and calibration manuals. Cross-Team Collaborative Delivery: Interface with perception, prediction and planning, vehicle electronic control, and testing teams to jointly complete full-chain joint debugging of autonomous driving; align with upstream planned trajectory outputs and downstream chassis execution characteristics to ensure that vehicle autonomous driving control logic is matched and functions are stably delivered.

Job Requirements Basic Requirements

Bachelor's degree or above in Vehicle Engineering, Automation, Control Engineering, Artificial Intelligence, Mechatronic Engineering, or related majors, with 5 years or more of experience in MBD development of autonomous driving/vehicle control algorithms. Proficient in the MBD development system; skilled in using Matlab/Simulink/Stateflow for control model construction, modeling, simulation, and code generation; and thoroughly familiar with MBD development processes and modeling standards. Familiar with vehicle control principles; mastery of core lateral and longitudinal autonomous driving control algorithms; understanding of the principles and engineering implementation of mainstream control algorithms such as PID, MPC, preview control, and adaptive control; familiarity with heavy-truck trailer dynamics and articulated body control logic is preferred.

Familiar with automotive embedded development, CAN bus signals, and vehicle electronic control architecture; with experience in model code generation and embedded deployment.

Core Specialized

Requirements (Key Matching Points for the Role) Heavy-truck/commercial-vehicle background preferred: candidates with experience in autonomous driving control algorithm development, MBD modeling, and real-vehicle calibration for heavy trucks and trailers are preferred; familiarity with the chassis dynamics characteristics of commercial vehicle + trailer combinations and the control pain points of articulated bodies. Ability to independently complete the full process of MBD modeling, simulation validation, and real-vehicle debugging of control modules, and to independently localize and resolve engineering issues such as real-vehicle control jitter, overshoot, and tracking deviation. Familiar with the MIL/SIL/HIL simulation testing process for autonomous driving; able to independently complete simulation iteration and scenario regression of control models.

Possess a solid engineering implementation mindset, not limited to algorithm theory, and able to adapt to real vehicle operating conditions to complete strategy optimization and parameter calibration.

Preferred Qualifications Experience in implementing autonomous driving control algorithms for line-haul logistics heavy trucks and highway pilot. Familiarity with commercial vehicle chassis dynamics, trailer attitude control, payload-adaptive control, anti-shimmy under extreme conditions, and anti-jackknifing stability control strategies.

Experience in adapting and joint-debugging control modules for end-to-end autonomous driving algorithms is preferred.