scholarly journals An Autonomous Driving Framework for Long-Term Decision-Making and Short-Term Trajectory Planning on Frenet Space

Author(s):  
Majid Moghadam ◽  
Gabriel Hugh Elkaim
2009 ◽  
Vol 9 (3) ◽  
pp. 9-19 ◽  
Author(s):  
Thomas Princen

A central conundrum in the need to infuse a long-term perspective into climate policy and other environmental decision-making is the widespread belief that humans are inherently short-term thinkers. An analysis of human decision-making informed by evolved adaptations—biological, psychological and cultural—suggests that humans actually have a long-term thinking capacity. In fact, the human time horizon encompasses both the immediate and the future (near and far term). And yet this very temporal duality makes people susceptible to manipulation; it carries its own politics, a politics of the short term. A “legacy politics” would extend the prevailing time horizon by identifying structural factors that build on evolved biological and cultural factors.


Author(s):  
Konstans Wells ◽  
Miguel Lurgi

AbstractThe rapid and pandemic spread of COVID-19 has led to unprecedented containment policies in response to overloaded health care systems. Disease mitigation strategies require informed decision-making to ensure a balance between the protection of the vulnerable from disease and the maintenance of global economies. We show that temporally restricted containment efforts, that have the potential to flatten epidemic curves, can result in wider disease spread and larger epidemic sizes in metapopulations. Longer-term rewiring of metapopulation networks or the enforcement of feasible long-term measures that decrease disease transmissions appear to be more efficient than temporarily restricted intensive mitigation strategies (e.g. short-term mass quarantine). Our results may inform balanced containment strategies for short-term disease spread mitigation in response to overloaded health care systems and longer-term epidemiological sizes.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maqsood Ahmad

PurposeThe purpose of this article is to clarify the mechanism by which underconfidence heuristic-driven bias influences the short-term and long-term investment decisions of individual investors, actively trading on the Pakistan Stock Exchange.Design/methodology/approachInvestors' underconfidence has been measured using a questionnaire, comprising numerous items, including indicators of short-term and long-term investment decision. In order to establish the influence of underconfidence on the investment decisions in both the short and long run, a 5-point Likert scale questionnaire has been used to collect data from the sample of 203 investors. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling technique.FindingsThis article provides further empirical insights into the relationship between heuristic-driven biases and investment decision-making in the short and long run. The results suggest that underconfidence bias has a markedly negative influence on the short-term and long-term decisions made by investors in developing markets. It means that heuristic-driven biases can impair the quality of both short-term and long-term investment decisions.Practical implicationsThis article encourages investors to avoid relying on cognitive heuristics, namely, underconfidence or their feelings when making short-term and long-term investment strategies. It provides awareness and understanding of heuristic-driven biases in investment management, which could be very useful for finance practitioners' such as investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making its financial management strategies. They can improve the quality of their decision-making by recognizing their behavioral biases and errors of judgment, to which we are all prone, resulting in more appropriate investment strategies.Originality/valueThe current study is the first to focus on links between underconfidence bias and short-term and long-term investment decision-making. This article enhanced the understanding of the role that heuristic-driven bias plays in the investment management and more importantly, it went some way toward enhancing understanding of behavioral aspects and their influence on the investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance specifically the role of heuristics in investment strategies; this field is in its initial stage, even in developed countries, while, in developing countries, little work has been done.


2018 ◽  
Vol 74 ◽  
pp. 06002
Author(s):  
Erikha Maurizka Mayzarah ◽  
Setyo Sarwanto Moersidik ◽  
Lana Saria

The use of chemical-based wastewater treatment method may cause harm to the environment. The aim of this research is to identify the flow application of wastewater treatment using phytoremediation method and summarize the perspective from stakeholders regarding the application based on long-term and short-term categories. In-depth interview was done with expert stakeholders from the sectors of government, private and academics. Subsequently, the scenario of wastewater treatment and the result of phytoremediation research that will be applied on nickel ore extraction industry were confirmed. Finally, a summary of the in-depth interview is shown on a form of a figure. The result shows that this method application needs effort and decision making from the company’s part that become the short-term issue for the company. However, on long-term side, phytoremediation is able to benefit the environment, social, and economy.


Author(s):  
Liting Sun ◽  
Cheng Peng ◽  
Wei Zhan ◽  
Masayoshi Tomizuka

Safety and efficiency are two key elements for planning and control in autonomous driving. Theoretically, model-based optimization methods, such as Model Predictive Control (MPC), can provide such optimal driving policies. Their computational complexity, however, grows exponentially with horizon length and number of surrounding vehicles. This makes them impractical for real-time implementation, particularly when nonlinear models are considered. To enable a fast and approximately optimal driving policy, we propose a safe imitation framework, which contains two hierarchical layers. The first layer, defined as the policy layer, is represented by a neural network that imitates a long-term expert driving policy via imitation learning. The second layer, called the execution layer, is a short-term model-based optimal controller that tracks and further fine-tunes the reference trajectories proposed by the policy layer with guaranteed short-term collision avoidance. Moreover, to reduce the distribution mismatch between the training set and the real world, Dataset Aggregation is utilized so that the performance of the policy layer can be improved from iteration to iteration. Several highway driving scenarios are demonstrated in simulations, and the results show that the proposed framework can achieve similar performance as sophisticated long-term optimization approaches but with significantly improved computational efficiency.


1973 ◽  
Vol 67 (1) ◽  
pp. 29-54 ◽  
Author(s):  
Howard Rosenthal ◽  
Subrata Sen

Variations in second ballot abstention and blank and invalid ballot rates (over the cross-section of French election districts) are examined for all four legislative elections of the French Fifth Republic. Analysis was conducted primarily through a heuristic decision-making model and a spatial model developed from the theories of Riker, McKelvey, and Ordeshook, and Davis, Hinich, and Ordeshook.Abstentions appear to be primarily influenced by long-term factors and the competitiveness of the contest. Blank ballots appear to be primarily dependent upon short-term factors, especially nonvoting from the alienation that results when a candidate present on the first ballot is not present on the second. The alienation model and the heuristic model, though partly collinear, make independent contributions to the explanation of the blank ballot variance.


Children ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. 139
Author(s):  
Ranjit Philip ◽  
Vineet Lamba ◽  
Ajay Talati ◽  
Shyam Sathanandam

There continues to be a reluctance to close the patent ductus arteriosus (PDA) in premature infants. The debate on whether the short-term outcomes translate to a difference in long-term benefits remains. This article intends to review the pulmonary vasculature changes that can occur with a chronic hemodynamically significant PDA in a preterm infant. It also explains the rationale and decision-making involved in a diagnostic cardiac catheterization and transcatheter PDA closure in these preterm infants.


Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 946
Author(s):  
Bohan Jiang ◽  
Xiaohui Li ◽  
Yujun Zeng ◽  
Daxue Liu

This paper presents a novel cooperative trajectory planning approach for semi-autonomous driving. The machine interacts with the driver at the decision level and the trajectory generation level. To minimize conflicts between the machine and the human, the trajectory planning problem is decomposed into a high-level behavior decision-making problem and a low-level trajectory planning problem. The approach infers the driver’s behavioral semantics according to the driving context and the driver’s input. The trajectories are generated based on the behavioral semantics and driver’s input. The feasibility of the proposed approach is validated by real vehicle experiments. The results prove that the proposed human–machine cooperative trajectory planning approach can successfully help the driver to avoid collisions while respecting the driver’s behavior.


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