Car collision avoidance with velocity obstacle approach

The obstacle avoidance maneuver is required for an autonomous vehicle. It is essential to define the system's performance by evaluating the minimum reaction times of the vehicle and analyzing the probability of success of the avoiding operation. This paper presents a collision avoidance algorithm based on the velocity bstacle approach that guarantees collision-free maneuvers. The vehicle is controlled by an optimal feedback control named FLOP, designed to produce the best performance in terms of safety and minimum kinetic collision energy. Dimensionless accident evaluation parameters are proposed to compare different crash scenarios

Exploring Smart Infrastructure Concepts to Improve the Reliability and Functionality of Safety Oriented Connected Vehicle Applications

Cooperative adaptive cruise control (CACC), a form of vehicle platooning, is a well known connected vehicle application. It extends adaptive cruise control (ACC) by incorporating vehicle-to-vehicle communications. A vehicle periodically broadcasts a small message that includes in the least a unique vehicle identifier, its current geo-location, speed, and acceleration. A vehicle might pay attention to the message stream of only the car ahead. While CACC is under intense study by the academic community, the vast majority of the relevant published literature has been limited to theoretical studies that make many simplifying assumptions. The research presented in this dissertation has been motivated by our observation that there is limited understanding of how platoons actually work under a range of realistic operating conditions. Our research includes a performance study of V2V communications based on actual V2V radios supplemented by simulation. These results are in turn applied to the analysis of CACC. In order to understand a platoon at scale, we resort to simulations and analysis using the ns3 simulator. Assessment criteria includes network reliability measures as well as application oriented measures. Network assessment involves latency and first and second order loss dynamics. CACC performance is based on stability, frequency of crashes, and the rate of traffic flow. The primary goal of CACC is to maximize traffic flow subject to a maximum allowed speed. This requires maintaining smaller inter-vehicle distances which can be problematic as a platoon can become unstable as the target headway between cars is reduced. The main contribution of this dissertation is the development and evaluation of two heuristic approaches for dynamically adapting headway both of which attempt to minimize the headway while ensure stability. We present the design and analysis of a centralized and a distributed implementation of the algorithm. Our results suggest that dynamically adapting the headway time can improve the overall platoon traffic flow without the platoon becoming unstable

Real-time Trajectory Planning to Enable Safe and Performant Automated Vehicles Operating in Unknown Dynamic Environments

Need for increased automated vehicle safety and performance will exist until control systems can fully exploit the vehicle's maneuvering capacity to avoid collisions with both static and moving obstacles in unknown environments. A safe and performance-based trajectory planning algorithm exists that can operate an automated vehicle in unknown static environments. However, this algorithm cannot be used safely in unknown dynamic environments; furthermore, it is not real-time. Accordingly, this thesis addresses two overarching research questions: * How should a trajectory planning algorithm be formulated to enable automated ground vehicle safety and performance in unknown dynamic environments? * How can such an algorithm be solved in real-time? Safe trajectory planning for high-performance automated vehicles with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time to goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This thesis presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a high mobility multipurpose wheeled vehicle (HMMWV), that includes the above set of specifications. This formulation is tested then with various sets of these specifications in a known dynamic environment. In particular, a parametric study relating execution horizon and obstacle speed reveals that the moving obstacle avoidance specification is not needed for safety when the planner has a short execution horizon (< 0.375 s), and the obstacles are slow (< 2.11 m/s). However, a moving obstacle avoidance specification is needed when the obstacles move faster, and this specification improves safety without, in most cases, increasing solve-times. Overall, results indicate that trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above unless a static or low-speed environment permits a less comprehensive planner. Then, this thesis combines this comprehensive planning algorithm with a suitable perception algorithm to enable safe and performant control of automated ground vehicles in unknown dynamic environments. A high-fidelity, ROS-based proving ground with a 2D LiDAR model, in Gazebo, and a 145 degree of freedom model of the HMMWV, in Chrono, is developed to combine these algorithms. Six-hundred tests, realized with various obstacle speeds and sizes, are performed in this proving ground in both known and unknown dynamic environments. Results from this comparison demonstrate that operating in an unknown environment, as opposed to a known environment, significantly increases collisions, steering effort, throttle effort, braking effort, orientation and tracking error, time to goal, and planner solve times. To avoid this deterioration of safety and performance factors in unknown environments, the use of more accurate perception systems should be explored. Ultimately, however, these results demonstrate that the comprehensive trajectory planning formulation developed in this thesis enables safe and performant control of automated vehicles in unknown dynamic environments among small (< 2 m) obstacles traveling at speeds up to high (20 m/s). To solve this formulation in real-time, an open-source, direct-collocation-based optimal control problem modeling language, called NLOptControl, is established in this thesis. Results demonstrate that NLOptControl can solve the formulation in real-time in both known and unknown environments. NLOptControl holds great potential for not only improving existing off-line and on-line control systems but also engendering a wide variety of new ones.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149859/1/febbo_1.pd

Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction

This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads

Real-Time Collision Imminent Steering Using One-Level Nonlinear Model Predictive Control

Automotive active safety features are designed to complement or intervene a human driver's actions in safety critical situations. Existing active safety features, such as adaptive cruise control and lane keep assist, are able to exploit the ever growing sensor and computing capabilities of modern automobiles. An emerging feature, collision imminent steering, is designed to perform an evasive lane change to avoid collision if the vehicle believes collision cannot be avoided by braking alone. This is a challenging maneuver, as the expected highway setting is characterized by high speeds, narrow lane restrictions, and hard safety constraints. To perform such a maneuver, the vehicle may be required to operate at the nonlinear dynamics limits, necessitating advanced control strategies to enforce safety and drivability constraints. This dissertation presents a one-level nonlinear model predictive controller formulation to perform a collision imminent steering maneuver in a highway setting at high speeds, with direct consideration of safety criteria in the highway environment and the nonlinearities characteristic of such a potentially aggressive maneuver. The controller is cognizant of highway sizing constraints, vehicle handling capability and stability limits, and time latency when calculating the control action. In simulated testing, it is shown the controller can avoid collision by conducting a lane change in roughly half the distance required to avoid collision by braking alone. In preliminary vehicle testing, it is shown the control formulation is compatible with the existing perception pipeline, and prescribed control action can safely perform a lane change at low speed. Further, the controller must be suitable for real-time implementation and compatible with expected automotive control architecture. Collision imminent steering, and more broadly collision avoidance, control is a computationally challenging problem. At highway speeds, the required time for action is on the order of hundreds of milliseconds, requiring a control formulation capable of operating at tens of Hertz. To this extent, this dissertation investigates the computational expense of such a controller, and presents a framework for designing real-time compatible nonlinear model predictive controllers. Specifically, methods for numerically simulating the predicted vehicle response and response sensitivities are compared, their cross interaction with trajectory optimization strategy are considered, and the resulting mapping to a parallel computing hardware architecture is investigated. The framework systematically evaluates the underlying numerical optimization problem for bottlenecks, from which it provides alternative solutions strategies to achieve real-time performance. As applied to the baseline collision imminent steering controller, the procedure results in an approximate three order of magnitude reduction in compute wall time, supporting real-time performance and enabling preliminary testing on automotive grade hardware.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163063/1/jbwurts_1.pd

High-Speed Obstacle Avoidance at the Dynamic Limits for Autonomous Ground Vehicles

Enabling autonomy of passenger-size and larger vehicles is becoming increasingly important in both military and commercial applications. For large autonomous ground vehicles (AGVs), the vehicle dynamics are critical to consider to ensure vehicle safety during obstacle avoidance maneuvers especially at high speeds. This research is concerned with large-size high-speed AGVs with high center of gravity that operate in unstructured environments. The term `unstructured' in this context denotes that there are no lanes or traffic rules to follow. No map of the environment is available a priori. The environment is perceived through a planar light detection and ranging sensor. The mission of the AGV is to move from its initial position to a given target position safely and as fast as possible. In this dissertation, a model predictive control (MPC)-based obstacle avoidance algorithm is developed to achieve the objectives through an iterative simultaneous optimization of the path and the corresponding control commands. MPC is chosen because it offers a rigorous and systematic approach for taking vehicle dynamics and safety constraints into account. Firstly, this thesis investigates the level of model fidelity needed for an MPC-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. Five different representations of vehicle dynamics models are considered. It is concluded that the two Degrees-of-Freedom (DoF) representation that accounts for tire nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm to operate the vehicle at its limits within an environment that includes large obstacles. Secondly, existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a novel nonlinear MPC formulation is developed. First, a new cost function formulation is used that aims to find the shortest path to the target position. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms of obstacle-free regions from an unstructured environment. Third, the no-wheel-lift-off condition is established offline using a fourteen DoF vehicle dynamics model and is included in the MPC formulation. The formulation can simultaneous optimize both steering angle and reference longitudinal speed commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different size and number. Thirdly, in the algorithm, a model of the vehicle is used explicitly to predict and optimize future actions, but in practice, the model parameter values are not exactly known. It is demonstrated that using nominal parameter values in the algorithm leads to safety issues in about one fourth of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the robustness of the algorithm, a novel double-worst-case formulation is developed. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation considering both the most likely and less likely worst-case scenarios renders the algorithm robust to all uncertainty realizations tested. The trade-off between the robustness and the task completion performance of the algorithm is also quantified. Finally, in addition to simulation-based validation, preliminary experimental validation is also performed. These results demonstrate that the developed algorithm is promising in terms of its capability of avoiding obstacles. Limitations and potential improvements of the algorithm are discussed.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135770/1/ljch_1.pd

Haptic Steering Interfaces for Semi-Autonomous Vehicles

Autonomous vehicles are predicted to significantly improve transportation quality by reducing traffic congestion, fuel expenditure and road accidents. However, until autonomous vehicles are reliable in all scenarios, human drivers will be asked to supervise automation behavior and intervene in automated driving when deemed necessary. Retaining the human driver in a strictly supervisory role, however, may make the driver complacent and reduce driver's situation awareness and driving skills which ironically, can further compromise the driver’s ability to intervene in safety-critical scenarios. Such issues can be alleviated by designing a human-automation interface that keeps the driver in-the-loop through constant interaction with automation and continuous feedback of automation's actions. This dissertation evaluates the utility of haptic feedback at the steering interface for enhancing driver awareness and enabling continuous human-automation interaction and performance improvement in semi-autonomous vehicles. In the first part of this dissertation, I investigate a driving scheme called Haptic Shared Control (HSC) in which the human driver and automation system share the steering control by simultaneously acting at the steering interface with finite mechanical impedances. I hypothesize that HSC can mitigate the human factors issues associated with semi-autonomous driving by allowing the human driver to continuously interact with automation and receive feedback about automation action. To test this hypothesis, I present two driving simulator experiments that are focused on the evaluation of HSC with respect to existing driving schemes during induced human and automation faults. In the first experiment, I compare obstacle avoidance performance of HSC with two existing control sharing schemes that support instantaneous transfers of control authority between human and automation. The results indicate that HSC outperforms both schemes in terms of obstacle avoidance, maneuvering efficiency, and driver engagement. In the second experiment, I consider emergency scenarios where I compare two HSC designs that provide high and low control authority to automation and an existing paradigm that decouples the driver input from the tires during collision avoidance. Results show that decoupling the driver invokes out-of-the-loop issues and misleads drivers to believe that they are in control. I also discover a `fault protection tradeoff': as the control authority provided to one agent increases, the protection against that agent's faults provided by the other agent reduces. In the second part of this dissertation, I focus on the problem of estimating haptic feedback from the road, or the road feedback. Road feedback is critical to making the driver aware of the state of the vehicle and road conditions, and its estimates are used in a variety of driver assist systems. However, conventional estimators only estimate road feedback on flat roads. To overcome this issue, I develop three estimators that enable road feedback estimation on uneven roads. I test and compare the performance of the three estimators by performing driving experiments on uneven roads such as road slopes and cleats. In the final part of this dissertation, I shift focus from physical human-automation interaction to human-human interaction. I present the evidence from the literature demonstrating that haptic feedback improves the performance of two humans physically collaborating on a shared task. I develop a control-theoretic model for haptic communication that can describe the mechanism by which haptic interaction facilitates performance improvement. The model creates a promising means to transfer the obtained insights to design robots or automation systems that can collaborate more efficiently with humans.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169975/1/akshaybh_1.pd

Fast and Safe Trajectory Optimization for Autonomous Mobile Robots using Reachability Analysis

Autonomous mobile robots (AMRs) can transform a wide variety of industries including transportation, shipping and goods delivery, and defense. AMRs must match or exceed human performance in metrics for task completion and safety. Motion plans for AMRs are generated by solving an optimization program where collision avoidance and the trajectory obeying a dynamic model of the robot are enforced as constraints. This dissertation focuses on three main challenges associated with trajectory planning. First, collision checks are typically performed at discrete time steps. Second, there can be a nontrivial gap between the planning model used and the actual system. Finally, there is inherent uncertainty in the motion of other agents or robots. This dissertation first proposes a receding-horizon planning methodology called Reachability-based Trajectory Design (RTD) to address the first and second challenges, where uncertainty is dealt with robustly. Sums-of-Squares (SOS) programming is used to represent the forward reachable set for a dynamic system plus uncertainty, over an interval of time, as a polynomial level set. The trajectory optimization is a polynomial optimization program over a space of trajectory parameters. Hardware demonstrations are implemented on a Segway, rover, and electric vehicle. In a simulation of 1,000 trials with static obstacles, RTD is compared to Rapidly-exploring Random Tree (RRT) and Nonlinear Model Predictive Control (NMPC) planners. RTD has success rates of 95.4% and 96.3% for the Segway and rover respectively, compared to 97.6% and 78.2% for RRT and 0% for NMPC planners. RTD is the only successful planner with no collisions. In 10 simulations with a CarSim model, RTD navigates a test track on all trials. In 1,000 simulations with random dynamic obstacles RTD has success rates of 96.8% and 100% respectively for the electric vehicle and Segway, compared to 77.3% and 92.4% for a State Lattice planner. In 100 simulations performing left turns, RTD has a success rate of 99% compared to 80% for an MPC controller tracking the lane centerline. The latter half of the dissertation treats uncertainty with the second and/or third challenges probabilistically. The Chance-constrained Parallel Bernstein Algorithm (CCPBA) allows one to solve the trajectory optimization program from RTD when obstacle states are given as probability functions. A comparison for an autonomous vehicle planning a lane change with one obstacle shows an MPC algorithm using Cantelli's inequality is unable to find a solution when the obstacle's predictions are generated with process noise three orders of magnitude less than CCPBA. In environments with 1-6 obstacles, CCPBA finds solutions in 1e-3 to 1.2 s compared to 1 to 16 s for an NMPC algorithm using the Chernoff bound. A hardware demonstration is implemented on the Segway. The final portion of the dissertation presents a chance-constrained NMPC method where uncertain components of the robot model are estimated online. The application is an autonomous vehicle with varying road surfaces. In the first study, the controller uses a linear tire force model. Over 200 trials of lane changes at 17 m/s, the chance-constrained controller has a cost 86% less than a controller using fixed coefficients for snow, and only 29% more than an oracle controller using the simulation model. The chance-constrained controller also has 0 lateral position constraint violations, while an adaptive-only controller has minor violations. The second study uses nonlinear tire models on a more aggressive maneuver and provides similar results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169729/1/skvaskov_1.pd