Relevance of the topics:

Connected Automated Driving Systems (ADS) are revolutionizing mobility by exchanging information that could be used for motion planning, perception enhancement, decision making, and vehicle stability controls in intelligent transportation settings [1]. 

Adaptive cruise control (ACC), as an example of advanced driver assistance systems (ADAS), is also benefiting from the resilience and robustness that connectivity brings in between different ADS in platooning [2], and more specifically urban driving. 

With ACC being prevalent for more than a decade and currently being incorporated for connected ADS, the potential for cooperative adaptive cruise control (CACC) strategies to become prominent for altering the emergent aspects of traffic flow has also been speculated [2]. Further, these strategies are also expected to stabilize, [3] for dissipating disturbances within the network.

Existing CACC frameworks, through dedicated short range communications (DSRC) and with the advancement of cellular 5G NR technologies, are shown to improve safety of the decision making and motion planning significantly [4]. 

The possible ways by which autonomous driving systems’ longitudinal dynamics could be stabilized [5] using the vehicle states (i.e., velocity and acceleration) shared among vehicles, in addition to onboard sensors for measuring the states of the vehicles nearby (e.g., relative position and speed) is of utmost importance for designing an effective ADS. 

Thus, reliable state estimation is essential for accurate navigation and controls in autonomous driving systems. Among the vehicle states required for decision making, motion planning, and path following in autonomous driving, information about the longitudinal and lateral speeds of the vehicle (i.e., slip ratio and sideslip) in the body frame is critical [2]. 

Existing visual- or inertial-based state estimators for advanced driver-assistance systems (ADAS) and autonomous driving are reaching their performance limits due to: vision based navigation challenges in uncertain urban environments with dynamic objects; perceptually degraded conditions [6]; or growing complexities in the model in presence of wheel slippage and tire force nonlinearities, impacting estimation error and update frequency in real-time. Longitudinal and lateral speeds can be measured with the global navigation satellite system (GNSS). 

However, loss of reception and low bandwidth of commercial GNSS in urban canyons, as well as high-slip scenarios for GPS-inertial fusion schemes [7] are primary challenges for reliable speed measurement in ADAS and intelligent vehicles Kinematic-based and tire force-based state observers are designed in the literature for speed/slip estimation robust to model uncertainties by using inertial measurement unit (IMU) and tire force measurement/estimates.

The kinematic based method utilizes stochastic observers [3] or visual-inertial based state observers [4]. Although kinematic-based state estimator for ADAS do not require tire forces, the longitudinal slip variation due to the wheel dynamics are not considered for observer development in the literature [8]. This results in inaccuracies for high-slip cases during harsh obstacle avoidance and acceleration/deceleration. 

The tire force-based approach utilizes IMU data and tire forces using Kalman, sliding mode, or nonlinear observers in presence of uncertainties in the vehicle model. Uncertainties include surface friction and tire parameters, and render the navigation framework susceptible to noises, especially for the wheel force saturation.

Hence the first part of this tutorial introduces the design procedure for a state observer, robust to road surface conditions, to estimate the longitudinal speed (and slip) which is essential for controls and safety critical decision making in autonomous driving.

Another pressing concern while designing a networked vehicular model, is that the longitudinal slip at each tire of the vehicle significantly affects not only the longitudinal forces, but also the lateral tires forces due to the combined-slip effect [9]. 

Thus, augmenting the error states in the networked model by the longitudinal slip information from the surrounding vehicle enhances stability and effectiveness of the longitudinal/lateral controls in vehicular networks. This will be studied with respect to the requirements of the Safety of the Intended Functionality (SOTIF). This tutorial includes the same by developing a slipaware networked vehicular model. 

Longitudinal dynamic controls for CACC will improve overall traffic safety by maintaining a consensual speed and a desired safety gap between adjacent vehicles, through minimization of the position error and velocity are achieved while guaranteeing stability. 

References:

[1] K. Bengler, K. Dietmayer, B. Farber, M. Maurer, C. Stiller, and H. Winner, “Three decades of driver assistance systems: Review and future perspectives,” IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 4, pp. 6–22, 2014.

[2] A. Talebpour and H. S. Mahmassani, “Influence of connected and autonomous vehicles on traffic flow stability and throughput,” Transportation Research Part C: Emerging Technologies, vol. 71, pp. 143– 163, 2016.

[3] P. E. Pare, E. Hashemi, R. Stern, H. Sandberg, and K. H. Johansson, ´ “Networked model for cooperative adaptive cruise control,” IFACPapersOnLine, vol. 52, no. 20, pp. 151–156, 2019. 

[4] C. Campolo, A. Molinaro, A. Iera, and F. Menichella, “5g network slicing for vehicle-to-everything services,” IEEE Wireless Communications, vol. 24, no. 6, pp. 38–45, 2017.

[5] B. Besselink and K. H. Johansson, “String stability and a delay-based spacing policy for vehicle platoons subject to disturbances,” IEEE Transactions on Automatic Control, vol. 62, no. 9, pp. 4376–4391, 2017.

[6] M. S. Ramanagopal, C. Anderson, R. Vasudevan, and M. Johnson Roberson, “Failing to learn: Autonomously identifying perception failures for self-driving cars,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3860–3867, 2018.

[7] S. Zhou, H. Zhao, W. Chen, Z. Miao, Z. Liu, H. Wang, and Y.-H. Liu, “Robust path following of the tractor-trailers system in gps-denied environments,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 500–507, 2019.

[8] Z. Yang and S. Shen, “Monocular visual–inertial state estimation with online initialization and camera–imu extrinsic calibration,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 1, pp. 39–51, 2016.

[9] E. Hashemi, X. He, and K. H. Johansson, “A dynamical game approach for integrated stabilization and path tracking for autonomous vehicles,” in 2020 American Control Conference (ACC). IEEE, 2020, pp. 4108– 4113.