Browse Topic: Cruise control
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid collisions under compromised conditions. It also analyzes the impact of varying levels of attack severity on overall system performance. The cooperative navigation framework highlights its potential as a robust solution for secure, efficient, and safe autonomous vehicle operations in increasingly interconnected and potentially hostile environments. Case 1 simulates communication jamming, where all channels except vehicle-to-vehicle communication are compromised. Case 2 extends this to jamming in the smart traffic light system, creating a non-signalized environment. Case 3 represents an ideal scenario with seamless communication. Case 4 explores vulnerability to deliberate interference in actor vehicle velocities, amplifying collision risk. Surrogate optimization with radial functions ensures proactive collision avoidance, while model predictive control with the interior point solver optimizes trajectory planning, promoting collision-free operation, and improving traffic flow. The algorithm’s outputs are seamlessly integrated into the vehicle control system, with the ego vehicle’s dynamics modeled realistically. Through extensive simulations, the algorithm proves effective across diverse scenarios, including communication disruptions and intentional interference. The research contributes to cooperative navigation system advancement, showcasing potential improvements in safety, efficiency, and adaptability in contemporary vehicular environments. The algorithm’s ability to handle various scenarios presents promising prospects for future intelligent transportation systems research.
The advancement of the automotive industry towards automation has fostered a growing integration between this field and automation. Future projects aim for the complete automation of the act of driving, enabling the vehicle to operate independently after the driver inputs the desired destination. In this context, the use of simulation systems becomes essential for the development and testing of control systems. This work proposes the control of an autonomous vehicle through fuzzy logic. Fuzzy logic allows for the development of sophisticated control systems in simple, easily maintainable, and low-cost controllers, proving particularly useful when the mathematical model is subject to uncertainties. To achieve this goal, the PDCA method was adopted to guide the stages of defining the problem, implementation, and evaluation of the proposed model. The code implementation was done in Python and validated using different looping scenarios. Three linguistic variables were used, one with three fuzzy sets. As a result, nine rules were implemented in order to evaluate the vehicle’s response. An iterative loop was proposed to model different acceleration, deceleration or speed maintenance scenarios. The implementation of a system controlled by fuzzy logic was performed using the Python programming language. The simulations validated the speed adjustment, proving to be efficient for applications in autonomous vehicles as a simple and low computational cost approach.
In recent decades, significant technological advances have made cruise control systems safer, more automated, and available in more driving scenarios. However, comparatively little progress has been made in optimizing vehicle efficiency while in cruise control. In this paper, two distinct strategies are proposed to deliver efficiency benefits in cruise control by leveraging flexibility around the driver’s requested set speed, and road information that is available on-board in many new vehicles. In today’s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions. Introducing even a small allowable “error band” around the set speed can allow the propulsion system to operate in a pseudo-steady state manner across most terrain. As long as the vehicle can remain in the allowed speed window, it can maintain a roughly constant load, traveling slower up hills and faster down hills. This strategy reduces the frequency of transient events (e.g. powertrain downshifts, enrichment following fuel cut-off) and the dramatic inefficiencies that result, particularly in ICE applications. The two strategies mentioned differ based on the propulsion control system’s knowledge of the anticipated elevation profile. Where upcoming elevation information is not known, a reactive strategy is used. This maintains efficient optimized steady-state operation for as long as possible, but then takes corrective action when vehicle speed approaches the boundaries of the allowed speed window. Where elevation of upcoming roads is known, a more capable predictive strategy is used. This can anticipate severe grades in advance and make milder corrections over longer time periods to avoid sharp transient behavior. Both strategies demonstrate that significant improvements in fuel economy and EV range can be achieved by relaxing the requirement that cruise control maintain a single constant speed at all times.
Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track. Finally, the tracking algorithm is tested on the vehicle equipped with millimeter radar and the result shows that the lateral and longitudinal position and velocity accuracy can satisfy the need for adaptive cruise control or other application.
Simulation of real time situations is a time tested software validation methodology in the automotive industry and array of simulation technologies have been in use for decades and is widely accepted and been part & parcel of software development cycle. While software that is being developed needs detailed plan, architecture and detailed design, it also matters during its development that, it is built in the right way from the very beginning and is fine tuned constantly. Especially for Software-In-Loop simulation (SIL), plenty of practices/tools/techniques/data are being used for simulation of system/software behavior. When it comes to choosing the right simulation technique and tools to be adopted, often there are discussions revolve around cost, feasibility, effectiveness, man-power, scalability, reusability etc. As automotive software validation is data driven, we deal with myriad of ground truth data for simulations, ranging from vehicle dynamics to vehicle models to environment factors (road, test track, weather). While infusing the ground truth data (physical data) in a simulation environment is still possible, availability of the same is often sparse, owing to test track constraints, availability of target objects, high risk maneuvers (involving higher vehicle speeds, narrow road curvatures, weather factors, accident reconstruction etc.) To tide over these constraints, in recent times, organization are inclined towards adopting virtual software simulation techniques, as it offers precision, scalability, cost and time effectiveness, and helps developers with early feedback to fine tune the software. CarMaker (Third Party Simulation tool) covers all the benefits stated above when it is integrated with the software (to be tested) in a closed loop. Vehicle data, Sensor data and real time scenarios (use cases) can be readily modelled for customer requirements for a product/SW and simulated and tested readily to provide to the developers on system/SW performance, ranging from ego dynamics, trajectory, camera/radar calibration, component/subsystem performance, accident severity, accident implications dealing with costs & damages, impact velocity, collision mitigation etc. Closed loop simulations are widely in use during development and testing of safety/cruising applications like ACC (Adaptive Cruise Control), EBA (Emergency Braking Assist), LCF (Lateral Control Functions) and also assists in functional areas of NCAP (New Car Assessment Programme) [1] and GIDAS (German In-Depth Accident Study) topics.
Automobile sector is growing every day with fast affinity towards Autonomous vehicles. The most challenging task of ADAS based driverless car is to identify and track the objects in front of the vehicle. To implement this type of technology we require a robust algorithm which can classify the object just-in-time and have great accuracy. We are using automotive radar sensor of 77GHz frequency. Quite often we’ve noticed sudden fluctuations in prediction of the obstacles using either heuristic or even machine learning techniques which focus only on frame-wise / cycle-wise data. So, this inspires us to investigate the history of the data coming in as opposed to only one cycle at a time. Hence, we incorporated a technique wherein we could make use of the past data as well as current cycle data. In this paper, we’ve used Radar time series data to classify the object in front of the Ego vehicle in each Radar cycle. The time series data collected from RADAR enables the reliable prediction of object to be an obstacle or not. Various Radar parameters are collected, analyzed, and fed into LSTM network, capable of handling order dependence, to predict whether the object in front is an obstacle or not. This will help in EBA and ACC functionality to avoid collisions and hazardous situations.
At present, the 77GHz millimeter-wave (MMW) radar is considered to be the most promising vehicle sensor in the automatic vehicle perception system. Although MMW radar is less affected by the weather and can reliably obtain information in bad weather, it does not mean that MMW radar is completely immune to weather. Aiming at the maximum detection range attenuation of the MMW radar in extreme weather, the article constructs the detection range attenuation model of the MMW radar in different weather conditions. Aiming at the impact of MMW detection attenuation on the environmental perception of autonomous driving, Autonomous Emergency Braking (AEB) and adaptive cruise control (ACC) algorithms are designed. We established the model and algorithm on the CARLA virtual simulation platform and simulated MMW radar detection attenuation to test the driving safety of automatic driving under different weather conditions. The simulation results show that MMW radar can well perceive the surrounding environment information under different weather conditions, ensure driving safety and realize the automatic driving function. However, there will be some differences in braking distance braking time, and the motion state of the vehicle is more vulnerable to the weather in the process of AEB.
In advanced driver assistance systems (ADAS) or autonomous driving Systems (ADS) the robust and reliable perception of the environment, especially for the detecting and tracking the surrounding vehicle is prerequisite for collision warning and collision avoidance. In this paper a post-fusion tracking approach is presented which combines the front view Radar observation and front smart camera information. The approach can improve the tracking accuracy of the tracking system to support ADAS or ADS function such as adaptive cruise control (ACC) or autonomous emergency braking (AEB). The paper describes the state estimation algorithm, data association in the fusion architecture. Furthermore, the fusion architecture is tested and validated in real highway driving scenario.
Vehicle speed controls, as adaptive cruise control and related automated evolutions, are control systems able to follow a desired vehicle reference speed that is set by the driver and fused with information as road signs, SD maps etc.. Current normal production systems don’t distinguish among the vehicle users, only some carmakers are doing first steps towards the introduction of learning from driver to adapt the traditional control. In our work, we follow up this content with a humanized speed control, based on learning of driver longitudinal behavior. This method is able to combine machine learning algorithms, vehicle positioning and recurrent trips into existing automated longitudinal control systems. Proposed algorithm can reduce the interactions between drivers and automated systems by improving the acceptance of automated longitudinal control. Furthermore, proposed integration works mainly on speed reference that dramatically simplifies the customization of the system. We present the general methodology of our online learning procedure and suggest how to integrate proposed work in a normal production vehicle.
In previous work, AC Compressor Cycling (ACC) was modeled by incorporating evaporator thermal inertia in Mobile Air Conditioning (MAC) performance simulation. Prediction accuracy of >95% in average cabin air temperature has been achieved at moderate ambient condition, however the number of ACC events in 1D CAE simulation were higher as compared to physical test [1]. This paper documents the systematic approach followed to address the challenges in simulation model in order to bridge the gap between physical and digital. In physical phenomenon, during cabin cooldown, after meeting the set/ target cooling of a cabin, the ACC takes place. During ACC, gradual heat transfer takes place between cold evaporator surface and air flowing over it because of evaporator thermal inertia. In earlier work, the ‘evaporator exit air temperature’ has been used to model ACC, whereas in the current work, the ‘evaporator exit air temperature’ is replaced by ‘point mass exit air temperature’ to simulate gradual heat transfer. Further, vehicle cabin and vents are modeled as point masses, which enables calibration of the cabin and AC vents independently with physical test results and capture rise/ fall in temperature precisely. Also, overall heat transfer coefficient, surface area and heat capacity impact are captured during correlation studies. With this approach, the target accuracy of >97% in average cabin air temperature and >90% in ACC frequency prediction has been achieved, which confirms the robustness of the simulation model. In proposed 1D CAE simulation model, AC compressor discharge and suction pressure correlation have limitation due to absence of point masses in digital model; being a software limitation, further work is required to address this gap in order to improve refrigerant pressure prediction accuracy.
Autonomous vehicle is a vehicle capable of sensing its environment and taking decisions automatically with no human interventions. To achieve this goal, ADAS (Advance Driving Assistance System) technologies play an important role and the technologies are improving and emerging. The sensing of environment can be achieved with the help of sensors like Radar and Camera. Radar sensors are used in detecting the range, speed and directions of multiple targets using complex signal processing algorithms. Radar with long range and short range are widely used in the autonomous vehicles. Radar sensors with long range can be used to realize features like Adaptive Cruise Control, Advance Emergency Brake Assist. The short-range radar sensors are used for Blind Spot Monitoring, Lane Change Assist, Rear/Front Cross Traffic Alert and Occupant Safe Exit. To realize the Autonomous vehicle functionalities four short range radar sensors are required, two on front and two on rear (left and right). This paper presents a detailed study on different solutions and methodologies for simulating radar signals and validation of short-range radar-based features. The various methodologies involve simulating of radar signals using Radar Target Simulator, Bypassing the radar sensing and injecting the radar raw signals, Bypassing the radar sensing injecting the target objects and Field and track testing in the real time environment. This paper also describes the challenges and comparison of different solutions.
The U.S. Environmental Protection Agency (EPA) certifies gasoline deposit control additives for intake valve deposit (IVD) control utilizing ASTM D5500, a vehicle test using a1985 BMW 318i. Concerns with the age of the test fleet, its relevance in the market today, and the availability of replacement parts led the American Chemistry Council’s (ACC) Fuel Additive Task Group (FATG) to begin a program to develop a replacement. General Motors suggested using a 2.4L LE9 test engine mounted on a dynamometer and committed to support the engine until 2030. Southwest Research Institute (SwRI®) was contracted to run the development program in four Phases. In Phase I, the engine test stand was configured, and a test fuel selected. In Phase II, a series of tests were run to identify a cycle that would build an acceptable level of deposits on un-additized fuel. In Phase III, the resultant test cycle was examined for repeatability. In Phases IVa and IVb, two discrimination matrices evaluated the response of additives on IVD levels. The results of Phase IVa indicated the EPA 65thpercentilefuel and test procedure combination did not compare with historical BMW results or replicate additive discrimination. The results of Phase IVb, using a TOP TIER™ certification fuel, showed a representative additive response in the LE9. ACC FATG considers the initial test development complete, but continued evaluation of the fuel, hardware, and test cycle will be required. With continued development in a Coordinating Research Council program, ACC FATG anticipates that the 9 2.4L IVD test can be standardized as an ASTM test method, and used as an alternate or replacement for the ASTM D5500 in both EPA and California Air Resources Board Reformulated Gasoline regulations. This would also position the 2.4L IVD test to become a replacement for the ASTM D6201 IVD test.
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