1,877 research outputs found

    Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

    Full text link
    In this study, we present novel work focused on assisting the driver during merge maneuvers. We use an automotive testbed instrumented with sensors for monitoring critical regions in the vehicle's surround. Fusing information from multiple sensor modalities, we integrate measurements into a contextually relevant, intuitive, general representation, which we term the Dynamic Probabilistic Drivability Map [DPDM]. We formulate the DPDM for driver assistance as a compact representation of the surround environment, integrating vehicle tracking information, lane information, road geometry, obstacle detection, and ego-vehicle dynamics. Given a robust understanding of the ego-vehicle's dynamics, other vehicles, and the on-road environment, our system recommends merge maneuvers to the driver, formulating the maneuver as a dynamic programming problem over the DPDM, searching for the minimum cost solution for merging. Based on the configuration of the road, lanes, and other vehicles on the road, the system recommends the appropriate acceleration or deceleration for merging into the adjacent lane, specifying when and how to merge

    Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

    Full text link
    Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

    TalkyCars: A Distributed Software Platform for Cooperative Perception among Connected Autonomous Vehicles based on Cellular-V2X Communication

    Get PDF
    Autonomous vehicles are required to operate among highly mixed traffic during their early market-introduction phase, solely relying on local sensory with limited range. Exhaustively comprehending and navigating complex urban environments is potentially not feasible with sufficient reliability using the aforesaid approach. Addressing this challenge, intelligent vehicles can virtually increase their perception range beyond their line of sight by utilizing Vehicle-to-Everything (V2X) communication with surrounding traffic participants to perform cooperative perception. Since existing solutions face a variety of limitations, including lack of comprehensiveness, universality and scalability, this thesis aims to conceptualize, implement and evaluate an end-to-end cooperative perception system using novel techniques. A comprehensive yet extensible modeling approach for dynamic traffic scenes is proposed first, which is based on probabilistic entity-relationship models, accounts for uncertain environments and combines low-level attributes with high-level relational- and semantic knowledge in a generic way. Second, the design of a holistic, distributed software architecture based on edge computing principles is proposed as a foundation for multi-vehicle high-level sensor fusion. In contrast to most existing approaches, the presented solution is designed to rely on Cellular-V2X communication in 5G networks and employs geographically distributed fusion nodes as part of a client-server configuration. A modular proof-of-concept implementation is evaluated in different simulated scenarios to assess the system\u27s performance both qualitatively and quantitatively. Experimental results show that the proposed system scales adequately to meet certain minimum requirements and yields an average improvement in overall perception quality of approximately 27 %

    Bayesian Nonparametric Feature and Policy Learning for Decision-Making

    Full text link
    Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario

    Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

    Full text link
    In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the KK most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the KK locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques

    Safety impact of connected and autonomous vehicles on motorways: a traffic microsimulation study

    Get PDF
    Connected and Autonomous Vehicles (CAVs) promise to improve road safety greatly. Despite the numerous CAV trials around the globe, their benefit has yet to be proven using real-world data. The lack of real-world CAV data has shifted the focus of the research community from traditional safety impact assessment methods to traffic microsimulation in order to evaluate their impacts. However, a plethora of operational, tactical and strategic challenges arising from the implementation of CAV technology remain unaddressed. This thesis presents an innovative and integrated CAV traffic microsimulation framework that aims to cover the aforementioned shortcomings.A new CAV control algorithm is developed in C++ programming language containing a longitudinal and lateral control algorithm that for the first time takes into consideration sensor error and vehicle platoon formulation of various sizes. A route-based decision-making algorithm for CAVs is also developed. The algorithm is applied to a simulated network of the M1 motorway in the United Kingdom which is calibrated and validated using instrumented vehicle data and inductive loop detector data. Multiple CAV market penetration rate, platoon size and sensor error rate scenarios are formulated and evaluated. Safety evaluation is conducted using traffic conflicts as a safety surrogate measure which is a function of time-to-collision and post encroachment time. The results reveal significant safety benefit (i.e. 10-94% reduction of traffic conflicts) as CAV market penetration increases from 0% to 100%; however, it is underlined that special focus should be given in the motorway merging and diverging areas where CAVs seem to face the most challenges. Additionally, it is proven that if the correct CAV platoon size is implemented at the appropriate point in time, greater safety benefits may be achieved. Otherwise, safety might deteriorate. However, sensor error does not affect traffic conflicts for the studied network. These results could provide valuable insights to policy makers regarding the reconfiguration of existing infrastructure to accommodate CAVs, the trustworthiness of existing CAV equipment and the optimal platoon size that should be enforced according to the market penetration rate.Finally, in order to forecast the conflict reduction for any given market penetration rate and understand the underlying factors behind traffic conflicts in a traffic microsimulation environment in-depth, a hierarchical spatial Bayesian negative binomial regression model is developed, based on the simulated CAV data. The results exhibit that besides CAV market penetration rate, speed variance across lanes significantly affects the production of simulated conflicts. As speed variance increases, the safety benefit decreases. These results emphasize the importance of speed homogeneity between lanes in a motorway as well as the increased risk in the motorway merging/diverging areas.</div
    • …
    corecore