###### NAVIGATE - BACK : [[CAR.builds|car.build]] ----- SELF driving drift car TO DO HEHE . BRUH lol this guy got it wrong : the project is CALLED : MARTY hahahaha https://news.stanford.edu/stories/2015/10/marty-autonomous-delorean-102015 https://news.stanford.edu/stories/2019/12/autonomous-delorean-drives-sideways-move-forward HERES technical research paper behind this https://asmedigitalcollection.asme.org/dynamicsystems/article/142/2/021004/1066044/Toward-Automated-Vehicle-Control-Beyond-the >[!quote]- NARU ><iframe allowfullscreen src="https://www.instagram.com/reel/C4stBkbChp_/embed" width="100%" height="555" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" ></iframe> > >https://www.instagram.com/reel/C4stBkbChp_/ ---- HERE what they say on how to implement such a system ## 1. **Choose and Prepare a Base Platform** - **Vehicle Selection:** Start with a rear-wheel–drive vehicle that has a chassis amenable to modification. In the paper, a 1981 DMC DeLorean was used (dubbed “MARTY”). The key is having a platform where you can: - Modify the drivetrain to allow independent control of the rear wheels. - Integrate an actuated steering system (e.g., electric power steering). - **Mechanical Reinforcement and Safety:** Since the vehicle will be operating near or beyond conventional handling limits, reinforce key structural components and integrate safety features (roll cage, harnesses, emergency cutoffs). --- ## 2. **Actuator and Drivetrain Modifications** - **Steering Actuation:** Replace or retrofit the mechanical steering with an electric power steering system that can be controlled via a computer. This will allow you to command steering angles (up to the necessary limit, e.g., ~38° as in the paper). - **Drivetrain and Wheel Actuation:** Equip the rear axle with independent electric motors (or modify the existing drivetrain) so that each rear wheel’s torque—and indirectly its wheelspeed—can be controlled individually. - This is crucial for implementing the cascaded wheelspeed controller that ensures the rear tire forces follow the desired force–slip relationship. --- ## 3. **Sensor Suite Integration** A robust sensor package is vital to measure the vehicle’s state accurately (position, velocity, yaw rate, sideslip, etc.): - **Global Positioning & Inertial Sensing:** - **RTK-GPS/IMU:** High-rate (e.g., 250 Hz) sensors like the Oxford Technical Systems RT4003 used in the study provide precise position and orientation data. - **Local State Sensing:** - **Wheel Speed Sensors:** To monitor and control the rotational speeds of each rear wheel. - **Additional Sensors:** Consider supplemental sensors (steering angle sensors, yaw rate sensors, accelerometers) to refine your state estimation. --- ## 4. **Computing Hardware and Real-Time Control** - **Real-Time Controller:** Use a real-time embedded computing platform (for example, a dSpace MicroAutoBoxII or similar automotive-grade system) that can: - Run control loops at high frequency (around 250 Hz or faster). - Interface with both sensors (for state estimation) and actuators (steering, motor drives). - **Data Acquisition and Communication:** Ensure that your system has low-latency data acquisition, sensor fusion (possibly employing Kalman filtering for accurate state estimation), and communication protocols for real-time control. --- ## 5. **Control Software and Algorithm Implementation** The heart of the project is the control architecture. The paper outlines a hierarchical controller with the following major elements: - **Vehicle Dynamics Modeling:** - **Single-Track “Bicycle” Model:** Develop a model that captures the essential dynamics (total velocity, sideslip angle, yaw rate) along with a curvilinear coordinate system for path tracking. - **Tire Force Models:** Implement models (e.g., Fiala brush model for the front tire and friction circle for the saturated rear tire) to accurately compute tire forces under drifting conditions. - **Controller Architecture:** - **Outer Loop – Drifting Controller:** - **Path Tracking:** Use the rotation rate of the velocity vector to track the lateral error relative to a desired path. - **Sideslip Stabilization:** Impose dynamics on the sideslip error (e.g., using a synthetic yaw rate input) to control how much the vehicle “drifts.” - **Nonlinear Model Inversion:** Map the desired state derivatives (yaw acceleration and course rate) to actuator commands (steering angle and a rear “thrust angle”). - **Inner Loop – Wheelspeed Controller:** - Instead of commanding rear axle torque directly, implement a low-level controller to regulate each wheel’s speed. - This loop ensures that the desired force–slip (and thus force) relationship is achieved quickly despite the inherent delays in wheel dynamics. - The control law (similar to Eq. (20) in the paper) takes into account feedforward terms from the desired rear longitudinal force and uses a first-order filtering strategy for smooth actuation. - **Reference Path and Trajectory Generation:** - Develop or adapt a path planning algorithm (like the “quasi-equilibrium” approach mentioned in the paper) that generates target curvature and sideslip profiles. - Use these profiles as feedforward inputs for your control system. - **Simulation and Software-in-the-Loop (SIL):** - Before integrating with the real vehicle, simulate the complete control architecture using tools like MATLAB/Simulink. - Validate your nonlinear model inversion, cascaded controller behavior, and robustness to disturbances in simulation. --- ## 6. **Integration, Calibration, and Safety** - **System Integration:** - Integrate the sensor suite, computing platform, and actuator controls. - Ensure that the interfaces between the high-level controller and low-level actuator loops are robust and that the timing constraints (control loop rates) are met. - **Calibration and Tuning:** - Tune control gains (for both the outer drift controller and inner wheelspeed controller) using simulation first, then iterative field testing. - Calibrate the sensor fusion algorithms to ensure accurate state estimates. - **Safety Protocols:** - Implement redundant safety systems such as emergency stop mechanisms and fallback (driver override) controls. - Conduct initial testing in controlled environments (e.g., closed courses or proving grounds) with appropriate safety measures in place. --- ## 7. **Testing and Experimental Validation** - **Progressive Testing:** - Begin with low-speed, limited-motion tests to validate sensor feedback and basic actuation. - Gradually increase the operating envelope to include drifting maneuvers as the controller shows reliable performance in simulation and controlled conditions. - **Data Collection and Analysis:** - Instrument the vehicle to record high-frequency data from all sensors and actuators. - Compare experimental results (such as lateral error, sideslip, yaw rate, and wheelspeed dynamics) against model predictions and refine your controller as necessary. --- ## Summary To “recreate” the car described in the paper, you must build a platform where mechanical modifications (steering, independent rear-wheel actuation), a high-precision sensor suite, a real-time computing platform, and sophisticated control algorithms (based on nonlinear model inversion and cascaded control) are integrated seamlessly. Start by: 1. **Selecting a vehicle** that you can modify. 2. **Installing actuators and sensors** to measure and command vehicle states. 3. **Developing a robust control system** in simulation (using the vehicle and tire models as in the paper) that handles both the outer-loop drift and path tracking and the inner-loop wheelspeed dynamics. 4. **Integrating and testing** everything in a controlled setting, iteratively refining the system. Each building block is essential for safely and reliably pushing the vehicle beyond conventional handling limits while maintaining control—a process that demands deep integration of mechanical, electrical, and software engineering expertise. By following these steps and iteratively validating your design through simulation and real-world testing, you’ll be well on your way to creating a vehicle capable of autonomous drifting along a complex trajectory as demonstrated in the referenced research.