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1. Drivers’ Spatio-Temporal Attentional Distributions Are Influenced by Vehicle Dynamics and Displayed Point of View

   https://doi.org/10.1177/0018720820902879  

   Morrison, Tyler_N; Rizzi, Emanuele; Turkkan, O_Anil; Jagacinski, Richard_J; Su, Haijun; Wang, Junmin (February 2020, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveThe aim of this study is to measure drivers’ attention to preview and their velocity and acceleration tracking error to evaluate two- and three-dimensional displays for following a winding roadway. BackgroundDisplay perturbation techniques and Fourier analysis of steering movements can be used to infer drivers’ spatio-temporal distribution of attention to preview. Fourier analysis of tracking error time histories provides measures of position, velocity, and acceleration error. MethodParticipants tracked a winding roadway with 1 s of preview in low-fidelity driving simulations. Position and rate-aided vehicle dynamics were paired with top-down and windshield displays of the roadway. ResultsFor both vehicle dynamics, tracking was smoother with the windshield display. This display emphasizes nearer preview positions and has a closer correspondence to the control-theoretic optimal attentional distributions for these tasks than the top-down display. This correspondence is interpreted as a form of stimulus–response compatibility. The position error and attentional signal-to-noise ratios did not differ between the two displays with position control, but with more complex rate-aided control much higher position error and much lower attentional signal-to-noise ratios occurred with the top-down display. ConclusionDisplay-driven influences on the distribution of attention may facilitate tracking with preview when they are similar to optimal attentional distributions derived from control theory. ApplicationDisplay perturbation techniques can be used to assess spatially distributed attention to evaluate displays and secondary tasks in the context of driving. This methodology can supplement eye movement measurements to determine what information is guiding drivers’ actions. 

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2. Anisotropic flocking control of distributed multi-agent systems using fluid abstraction

   Silic, M.; Song, Z.; Mohseni, K. (January 2018, Proceedings of the AIAA Information Systems-AIAA Infotech @ Aerospace)

   This paper presents a multi-agent flocking scheme for real-time control of homogeneous unmanned aerial vehicles (UAVs) based on smoothed particle hydrodynamics. Swarm cohe- sion, collision avoidance, and velocity consensus are concurrently satisfied by characterizing the emerging macroscopic flock as a continuous fluid. Two vital implementation issues are addressed in particular including latency in information fusion and directionality of com- munication due to antenna patterns. Symmetric control forces are achieved by meticulous scheduling of inter-vehicle communication to sustain the motion stability of the flock. A gener- alized, anisotropic smoothing kernel that takes into account the relative position and attitude between agents is adopted to address potential flocking instability introduced by communi- cation anisotropy due to the antenna radiation pattern. The feasibility of the technique is demonstrated experimentally using a single UAV avoiding a virtual obstacle. 

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3. Two-dimensional active sensing system for bicyclist-motorist crash prediction

   https://doi.org/10.23919/ACC.2017.7963298  

   Jeon, Woongsun; Rajamani, Rajesh (May 2017, American Control Conference (ACC), 2017)

   This paper develops an active sensing system for a bicycle to accurately track rear vehicles that can have two-dimensional motion. The active sensing system consists of a single-beam laser sensor mounted on a rotationally controlled platform. The sensing system is inexpensive, small, lightweight, consumes low power, and is thus ideally suited for the bicycle application. 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 illustrated first using simulation results. Then, preliminary experimental results are presented using an instrumented bicycle to show the feasibility of the system in tracking rear vehicles during both straight and turning maneuvers. 

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4. Deep-Learning-Based Intrusion Detection for Autonomous Vehicle-Following Systems

   Wang, S; Wu, S; Lin, C; Boddupalli, S; Lin, C; Shih, C; Ray, S. (January 2021, 4th IEEE International Conference on Intelligent Transportation Systems - ITSC2021)

   null (Ed.)

   Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder-based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches. 

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5. Discrete Time Optimal Trajectory Generation and Transversality Condition with Free Final Time

   https://doi.org/10.1109/ICUAS51884.2021.9476719  

   Eslamiat, Hossein; Sanyal, Amit K.; Lindsay, Clark (June 2021, 2021 International Conference on Unmanned Aircraft Systems)

   A discrete time, optimal trajectory planning scheme for position trajectory generation of a vehicle is given here, considering the mission duration as a free variable. The vehicle is actuated in three rotational degrees of freedom and one translational degree of freedom. This model is applicable to vehicles that have a body-fixed thrust vector direction for translational motion control, including fixed-wing and rotorcraft unmanned aerial vehicles (UAVs), unmanned underwater vehicles (UUVs) and spacecraft. The lightweight scheme proposed here generates the trajectory in inertial coordinates, and is intended for real time, on-the-go applications. The unspecified terminal time can be considered as an additional design parameter. This is done by deriving the optimality conditions in a discrete time setting, which results in the discrete transversality condition. The trajectory starts from an initial position and reaches a desired final position in an unspecified final time that ensures the cost on state and control is optimized. The trajectory generated by this scheme can be considered as the desired trajectory for a tracking control scheme. Numerical simulation results validate the performance of this trajectory generation scheme used in conjunction with a nonlinear tracking control scheme. 

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