This paper focuses on the development and use of a nonlinear observer for tracking of vehicle motion trajectories while using a radar sensor. Previous results on vehicle tracking have typically used an interacting multiple model filter that needs different models for different modes of vehicle motion. This paper uses a single nonlinear vehicle model that can be used for all modes of vehicle motion. Previous nonlinear observer design results from the literature do not work for the nonlinear system under consideration due to the wide range of operating conditions that need to be accommodated. Hence, a new nonlinear observer design technique that utilizes better bounds on the coupled nonlinear functions in the dynamics is developed. The exponential stability of the observer is established using Lyapunov techniques. The observer design with the developed technique is then implemented in both simulations and experiments. Experimental results show that the observer can simultaneously and accurately estimate longitudinal position, lateral position, and velocity and orientation variables for the vehicle from radar measurements. Results are presented both for tracking of vehicle maneuvers on highways and of maneuvers on urban roads at traffic intersections where turns and significant changes in vehicle orientation can occur.

This paper focuses on the detection of cyber-attacks on a wireless communication channel and simultaneous radar sensor health monitoring for a connected vehicle. A semi-autonomous adaptive cruise control (SA-ACC) vehicle is considered which has wireless communication with its immediately preceding vehicle in the same lane. The wireless connectivity enables the vehicle to operate at small time-gap distances without creating string instability. However, the reliability of the wireless connectivity is critical for ensuring safe vehicle operation. The presence of two unknown inputs related to both sensor failure and cyber-attack seemingly poses a difficult estimation challenge which is addressed in this paper. The dynamic system is first represented in descriptor system form. An observer with estimation error dynamics decoupled from the cyber-attack signal is developed. The performance of the observer is first extensively evaluated in simulations. The estimation system is able to detect either a fault in the radar or a cyber-attack. Further, the proposed control strategy achieves resilient adaptive cruise control system operation by switching to larger time-gaps and a non-connected controller when a cyber-attack is detected. Finally, experimental data from a real vehicle on the I-94 Highway in Minneapolis, MN, USA is also used to evaluate the performance of the proposed algorithms. The fundamental estimation algorithm developed herein can be extended in the future to enable cyber-attack detection in more complex connected vehicle architectures.

This article addresses the problem of state estimation and simultaneous learning of the vehicle's tire model on autonomous vehicles. The problem is motivated by the fact that lateral distance measurements are typically available on modern vehicles while tire models are difficult to identify and also vary with time. Tire forces are modeled in the estimator using a neural network in which no a priori assumptions on the type of model need to be made. A neuro-adaptive observer that provides asymptotically stable estimation of the state vector and of the neural network weights is developed. The developed observer is evaluated using both MATLAB simulations with a low-order model as well as with an unknown high-order model in the commercial software CarSim. Cornering and lane change maneuvers are used to learn the tire model over an adequately large range of slip angles. Performance with the low-order vehicle model is excellent with near-perfect estimation of states as well as the tire force nonlinear characteristics. The performance with the unknown high-order CarSim model is also found to be good with the tire model being estimated correctly over the range of slip angles excited by the executed vehicle maneuvers. The developed technology can enable a new approach to obtaining tire models that are otherwise difficult to identify in practice and depend on empirical characterizations.

This paper focuses on the development of an active sensing system for a bicycle to accurately detect and track rear vehicles. A collision detection sensor on a bicycle is required to be inexpensive, small, and lightweight. A single beam laser sensor that meets these constraints is mounted on a rotationally controlled platform for this sensing mission. The rotational orientation of the laser sensor needs to be actively controlled in real time in order to continue to focus on a rear vehicle, as the vehicle's lateral and longitudinal distances change. This tracking problem requires controlling the real-time angular position of the laser sensor without knowing the future trajectory of the vehicle. The challenge is addressed using a novel receding horizon framework for active control and an interacting multiple model framework for estimation. The features and benefits of this active sensing system are shown first using simulation results. Then, extensive experimental results are presented using an instrumented bicycle to show the performance of the system in detecting and tracking rear vehicles during both straight and turning maneuvers.

This paper focuses on the development of a system to detect if a nearby car poses a collision danger to a bicycle, and to sound a loud horn to alert the car driver if a collision danger is detected. A sensing and estimation system suitable for use on a bicycle is therefore developed in order to track the trajectories of vehicles in a traffic intersection. An inexpensive solid-state low-density Lidar mounted at the front of an instrumented bicycle is used. The low angular resolution of the sensor creates many challenges. These challenges are addressed in this research by clustering based approaches for assigning measurement points to individual vehicles, by introducing a correction term with its own dynamic model for position measurement refinement, and by incorporating multi-target tracking using global nearest neighbor data association and interacting multiple model extended Kalman filtering. The tracking performance of the developed system is evaluated by both simulation and experimental results. Scenarios that involve straight driving (in all four directions) and left turning (opposing) vehicles at a traffic intersection are considered. Experimental results show that the developed system can successfully track cars accurately in these scenarios in spite of the low measurement resolution of the sensor.