Environment perception based on LIDAR sensors for real road applications

The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.The work reported in this paper has been partly funded by the Spanish Ministry of Science and Innovation (TRA2007- 67786-C02-01, TRA2007-67786-C02-02, and TRA2009- 07505) and the CAM project SEGVAUTO-II.Publicad

Energy efficiency by thermal spraying

Machines used by the basic industries of Russia (metallurgy, oil-gas processing and power engineering) are characterized by high metal consumption, which in turn lead to high energy consumption. Their service lives increase and their worn parts are restored by means of low-cost materials for restoration. The processes of Thermal Spraying (TS) are more effective than alternative methods in restoration and increase in service life. Such methods include electroplating, chemical and thermal processing. The results of research and experience have proven this. At the same time, the load on the environment is reduced in comparison with the alternatives by decreasing emissions. Based on OEM publications and our own experience, we analysed the efficiency of TS processes. Plasma spraying was excluded from consideration since it has no real alternative for refractory oxide coatings which are mainly used for gas turbines and jet engines. By the criteria of the coating's quality, performance and cost arc of spraying, flame spraying, HVOF/HVAF, cold spraying and detonation spraying were compared. Commonly used materials for TS, such as metals and carbides in metal bond (cermet), were examined as sprayed materials. This paper shows that a combination of activated arc spraying and HVAF for producing wear and corrosion-resistant coating is a rational variant with respect to a wide variety of parts for the aforementioned industries. Examples of resource-saving in metallurgy, oil-gas processing and power engineering are shown based on our own TS experience in material, equipment and technology development. © 2014 WIT Press.International Journal of Safety and Security Engineering;International Journal of Sustainable Development and Planning;WIT Transactions on Ecology and the Environmen

Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios

Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems

Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NOx). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment

Predictive Collision Management for Time and Risk Dependent Path Planning

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.Comment: Extended version of the SIGSPATIAL '20 pape

Between environmental perception and decision-making: compositional engineering of safe automated driving systems

Development of autonomous vehicles has hit a slump in the past years. This slump is caused by the so-called approval trap for autonomous vehicles: While the industry has mostly mastered the methods for building autonomous vehicles, reliable mechanisms for ensuring their safety are still missing. It is generally accepted that the brute-force approach of driving enough mileage for documenting the relatively higher safety of autonomous vehicles (compared to human drivers) is not feasible. Since, as of today, no alternative strategies for the safety approval of autonomous vehicles exist. One promising strategy is decomposition of safety validation into many sub-tasks with compositional sub-goals (akin to safety cases but for a vehicles intended functionality) for replacing mileage by combining validation tasks that together document safety. A prerequisite for this strategy is that the required performance of each component can be specified and shown. Specifying how accurate an environmental perception needs to be, however, is a non-trivial task. Whether perceptual inaccuracies, like a wrongly classified or missing object, also lead to hazardous behavior can only be evaluated when considering both the residual processing chain and the operational situation the autonomous vehicle is in. This thesis proposes a formal approach for the validation of perception components consisting of three consecutive steps: creation of a taxonomy regarding perception component inaccuracy, elicitation of verifiable requirements for perception components regarding these inaccuracies and evaluation of the elicited requirements. To that end, we firstly touch on the specification of perception errors and propose an approach to determine relevance of objects in urban areas. Secondly, we elicit verifiable perception requirements subject to a given decision-making module in different scenarios by structured testing in a simulation framework. Finally, we deal with the evaluation of perception components. This includes our approach for the generation of dimension and classification reference values and an exemplary evaluation of an object detection module regarding relevant errors and our previously elicited requirements. To the best of our knowledge, this is the first time that a coherent, formal approach for a decomposed safety validation of perception components is proposed and demonstrated. We conclude, that our contributions provide a novel perspective on the interface between perception and decision-making and thus further support the idea of a decomposed safety validation for automated driving systems