Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework

Revolutionizing Urban Mobility: A Data-Driven Approach to Traffic Forecasting and Management

This paper, titled "Revolutionizing Urban Mobility," focuses on data-driven traffic forecasting and management to address the challenge of sustainable urban transportation. It employs deep learning, particularly LSTM neural networks, to predict traffic conditions and reduce congestion. The research includes comprehensive literature surveys, highlighting the effectiveness of machine learning and deep learning in traffic forecasting. The proposed methodology, based on linear regression, offers promising results with low error rates. Overall, this paper presents a valuable contribution to enhancing urban mobility through data-driven approaches

Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd

Modeling driver distraction mechanism and its safety impact in automated vehicle environment.

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly pre-defined distraction as a binary outcome and investigated the patterns that contribute to distraction from multiple perspectives. However, the magnitude of AV distraction is not accurately quantified. Moreover, past studies in quantifying distraction have mainly used wearable sensors’ data. In reality, it is not realistic for drivers to wear these sensors whenever they drive. Hence, a research motivation is to develop a surrogate model that can replace the wearable device-based data to predict AV distraction. From the safety perspective, there lacks a comprehensive understanding of how AV distraction impacts safety. Furthermore, a solution is needed for safely offsetting the impact of distracted driving. In this context, this research aims to (1) improve the existing methods in quantifying Human-Vehicle Interaction-induced (HVI-induced) driver distraction under automated driving; (2) develop a surrogate driver distraction prediction model without using wearable sensor data; (3) quantitatively reveal the dynamic nature of safety benefits and collision hazards of HVI-induced visual and cognitive distractions under automated driving by mathematically formulating the interrelationships among contributing factors; and (4) propose a conceptual prototype of an AI-driven, Ultra-advanced Collision Avoidance System (AUCAS-L3) targeting HVI-induced driver distraction under automated driving without eye-tracking and video-recording. Fixation and pupil dilation data from the eye tracking device are used to model driver distraction, focusing on visual and cognitive distraction, respectively. In order to validate the proposed methods for measuring and modeling driver distraction, a data collection was conducted by inviting drivers to try out automated driving under Level 3 automation on a simulator. Each driver went through a jaywalker scenario twice, receiving a takeover request under two types of HVI, namely “visual only” and “visual and audible”. Each driver was required to wear an eye-tracker so that the fixation and pupil dilation data could be collected when driving, along with driving performance data being recorded by the simulator. In addition, drivers’ demographical information was collected by a pre-experiment survey. As a result, the magnitude of visual and cognitive distraction was quantified, exploring the dynamic changes over time. Drivers are more concentrated and maintain a higher level of takeover readiness under the “visual and audible” warning, compared to “visual only” warning. The change of visual distraction was mathematically formulated as a function of time. In addition, the change of visual distraction magnitude over time is explained from the driving psychology perspective. Moreover, the visual distraction was also measured by direction in this research, and hotspots of visual distraction were identified with regard to driving safety. When discussing the cognitive distraction magnitude, the driver’s age was identified as a contributing factor. HVI warning type contributes to the significant difference in cognitive distraction acceleration rate. After drivers reach the maximum visual distraction, cognitive distraction tends to increase continuously. Also, this research contributes to quantitatively revealing how visual and cognitive distraction impacts the collision hazards, respectively. Moreover, this research contributes to the literature by developing deep learning-based models in predicting a driver’s visual and cognitive distraction intensity, focusing on demographics, HVI warning types, and driving performance. As a solution to safety issues caused by driver distraction, the AUCAS-L3 has been proposed. The AUCAS-L3 is validated with high accuracies in predicting (a) whether a driver is distracted and does not perform takeover actions and (b) whether crashes happen or not if taken over. After predicting the presence of driver distraction or a crash, AUCAS-L3 automatically applies the brake pedal for drivers as effective and efficient protection to driver distraction under automated driving. And finally, a conceptual prototype in predicting AV distraction and traffic conflict was proposed, which can predict the collision hazards in advance of 0.82 seconds on average