Browse Topic: Lane keeping assistance
ABSTRACT In any active safety system, it is desired to measure the “performance”. For the estimation case, generally a cost function like Mean-Square Error is used. For detection cases, the combination of Probability of Detection and Probability of False Alarm is used. Scenarios that would really expose performance measurement involve complex, dangerous and costly driving situations and are hard to recreate while having a low probability of actually being acquired . Using a virtual tool, we can produce the trials necessary to adequately determine the performance of active safety algorithms and systems. In this paper, we will outline the problem of measuring the performance of active safety algorithms or systems. We will then discuss the approach of using complex scenario design and Monte Carlo techniques to determine performance. We then follow with a brief discussion of Prescan and how it can help in this endeavor. Finally, two Monte Carlo type examples for particular active safety
The purpose of this document is to provide guidance for the implementation of DVI for momentary intervention-type LKA systems, as defined by ISO 11270. LKA systems provide driver support for safe lane keeping operations via momentary interventions. LKA systems are SAE Level 0, according to SAE J3016. LKA systems do not automate any part of the dynamic driving task (DDT) on a sustained basis and are not classified as an integral component of a partial or conditional driving automation system per SAE J3016. The design intent (i.e., purpose) of an LKA system is to address crash scenarios resulting from inadvertent lane or road departures. Drivers can override an LKA system intervention at any time. LKA systems do not guarantee prevention of lane drifts or related crashes. Road and driving environment (e.g., lane line delineation, inclement weather, road curvature, road surface, etc.) as well as vehicle factors (e.g., speed, lateral acceleration, equipment condition, etc.) may affect the
This SAE Recommended Practice presents a method and example results for determining the Automotive Safety Integrity Level (ASIL) for automotive motion control electrical and/or electronic (E/E) systems. The ASIL determination activity is required by ISO 26262-3, and it is intended that the process and results herein are consistent with ISO 26262. The technical focus of this document is on vehicle motion control systems. The scope of this SAE Recommended Practice is limited to collision-related hazards associated with motion control systems. This SAE Recommended Practice focuses on motion control systems since the hazards they can create generally have higher ASIL ratings, as compared to the hazards non-motion control systems can create. Because of this, the Functional Safety Committee decided to give motion control systems a higher priority and focus exclusively on them in this SAE Recommended Practice. ISO 26262 has a wider scope than SAE J2980, covering other functions and accidents
This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for six levels of driving automation, ranging from no driving automation (Level 0) to full driving automation (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No Driving Automation Level 1: Driver Assistance Level 2: Partial Driving Automation Level 3: Conditional Driving Automation Level 4: High Driving Automation Level 5: Full Driving Automation These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit
The increase of autonomy demand in the automotive industry made the usage of AI models inevitable. However, such models introduce a variety of threats to automobile safety and security. ISO/PAS 21448 SOTIF is a safety standard that is designed to deal with risks due to non-electrical and non-electronic failures. In this paper we put SOTIF into practice. In our work we introduce a conceivable safety critical scenario that targets the lane keep assist function. We use the suggested modelling techniques in the SOTIF standard to analyze the scenario and extract the trigger event. In result, we propose a contextual based predictive ML model to monitor the intervention between the driver and lane keep assist system. Our approach followed the SOTIF verification and validation guidelines. Empirically, we use a real safety critical scenario dataset as well as an augmented dataset. Our results show a high precision/recall values that exceed 90% by an increase of more than 150% in f1 score
Implementing Advanced Driver Assistance Systems (ADAS) features that are available in all road scenarios and weather conditions is a big challenge for automotive companies and considered key enablers to achieve autonomous Level 4 (L4) vehicles. One important feature is the Lane Keep Assist System (LKAS). Most LKAS systems are based on lane line detection cameras and lane coefficient estimations by the camera is the key point for LKAS where the camera recognizes the lane lines using edge detection. But when the lane markers are not available due to high traffic and slow driving on the roads, another source of data for the lane lines needs to be available for the LKAS. In this paper a multi-sensor fusion approach based on camera, Lidar, and GPS is used to allow the vehicle to maintain its lateral location within the lane. The lateral distances of the lane lines are measured by LiDAR detection of the markers based on the intensity and fused with lane line information from the HD Map after
A veteran tester who has datalogged many thousands of miles in the U.S. and Japan offers suggestions for rapidly acquiring good test and validation data. Testing for advanced driver-assist systems (ADAS) has required a completely new approach to testing. The most obvious reason for this is the sheer number of sensors and actuators involved in any given feature. Whereas engineers used to test single sensors at a time, today there are upward of 20 sensors to specify. These sensors - short- and long-range radar, mono/stereo cameras, sonar and lidar - need to act in concert. This means that several layers of sensor fusion must be implemented. For example, when it comes to the actuators for the braking system, there are up to seven distinct systems able to apply the brakes. As a result, a failure is not easily tracked to a single root cause but can be a complex combination of several factors. Imagine expanding this to ADAS and autonomous-vehicle (AV) functionalities such as automated cruise
Lane keeping assist system (LKAS) is an advanced assistant driving system, which can effectively avoid unconscious lane departures caused by drivers due to distraction, fatigue driving, insensitive response to emergencies, etc., reduce traffic accidents, and effectively improve Driving safety. This paper is committed to the research of LKAS standard of commercial vehicles in China. According to the working principle of LKAS and the test and evaluation methods of LKAS at home and abroad, the LKAS test scheme of commercial vehicles was designed after repeated discussions and demonstrations in many meetings, including straight and curve test scenarios. The actual vehicle test of commercial vehicle LKAS were completed by CATARC Automotive Test Center (Tianjin) Co., Ltd. in CATARC Yancheng Automotive Proving Ground Co., Ltd. The performance of LKAS of freight cars and passenger cars under different loads was studied. Based on the test results, suggestions on the formulation of domestic LKAS
Lane keeping assist (LKA) is an autonomous driving technique that enables vehicles to travel along a desired line of lanes by adjusting the front steering angle. Reinforcement learning (RL) is one kind of machine learning. Agents or machines are not told how to act but instead learn from interaction with the environment. It also frees us from coding complex policies manually. But it has not yet been successfully applied to autonomous driving. Two control strategies using different deep reinforcement learning (DRL) algorithms have been proposed and used in the lane keeping assist scenario in this paper. Deep Q-network (DQN) algorithm with discrete action space and deep deterministic policy gradient (DDPG) algorithm with continuous action space have been implemented, respectively. Based on MATLAB/Simulink, deep neural networks representing the control policy are designed. The environment as well as the vehicle dynamics are also modelled in Simulink. By integrating the proposed control
In recent decades, research and development in the field of autonomous vehicles have rapidly increased throughout the world, and autonomous driving technologies have begun to be applied to mass production vehicles. Especially recently, even affordable mass production vehicles have begun to be equipped with some autonomous driving systems such as a Lane Keeping Assist (LKA) system. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage or blurred lane markings. To overcome this limitation, we have developed Global Navigation Satellite System (GNSS)-based LKA systems, which are not affected by the surrounding environment such as weather and the condition of lane markings. In our latest study, we applied Model Predictive Control (MPC) to our
Advanced Driver Assistance Systems (ADAS) like Lane Departure Warning (LDW) and Lane Keep Assist (LKA) have been available for several years now but has experienced low customer acceptance and market penetration. These deficiencies can be traced to the inability of many of the perception systems to consistently recognize lane markings and localize the vehicle with respect to the lane markings in the real-world with poor markings, changing weather conditions and occlusions. Currently, there is no available standard or benchmark to evaluate the quality of either the lane markings or the perception algorithms. This work seeks to establish a reference test system that could be used by transportation agencies to evaluate the quality of their markings to support ADAS functions that rely on pavement markings. The test system can also be used by designers as a benchmark for their proprietary systems. To support this development, an extensive video dataset was collected at different times of
Lane Keeping Assistance System (LKAS) is a typical lateral driver assistance system with low acceptance. One of the main reasons is that fixed parameters cannot satisfy individual differences. So LKAS adaptive to driver characteristics needs to be designed. Driver Steering Override (DSO) process is an important process of LKAS. It happens when contradiction between driver’s intention and system behavior occurs. As feeling of overriding will affect the overall experience of using LKAS, the design of DSO characteristics is worthy of attention. This research provided an adaptive design scheme aiming at DSO characteristics for LKAS by building Driver Preference Model (DPM) based on simulator test data from preliminary experiments. The DPM was to represent the relationship between driver characteristics indices and driver preferred system characteristics indices. So that new drivers’ preference can be predicted by DPM based on their own daily driving data with LKAS switched off. The inputs
Objectives: The project goal was to create an initial set of standardized tests to explore whether they enable the ongoing evaluation of automated driving features as they evolve over time. These tests focused on situations that were representative of several daily driving scenarios as encountered by lower-level automated features, often called Advanced Driver Assistance Systems (ADAS), while looking forward to higher levels of automation as new systems are deployed. Methods: The research project initially gathered information through a review of existing literature about ADAS and current test procedures. Thereafter, a focus group of industry experts was convened for additional insights and feedback. With this background, the research team developed a series of tests designed to evaluate a variety of automated driving features in currently available implementations and anticipated future variants. Key ADAS available on current production vehicles include adaptive cruise control (ACC
Lane-keeping assist system (LKA) alerts the driver or intervenes in the driving when the vehicle deviates from the lane. But its effect is highly dependent on the driver’s acceptance. Distance to Lane Crossing (DTLC) and Time to Lane Crossing (TTLC) are two important factors to consider the danger level of the scenario, which are also two references for drivers to make decisions. At present, most of the functional design standards are based on these values, while they often differ for different vehicle movements. This study uses a driving robot to precisely control the test conditions and performs field tests on two advanced autonomous vehicles in National Intelligent Connected Vehicle (Shanghai) Pilot Zone. The test conditions are extended based on various test standards and the LKA performance of vehicles in the pre-experiment. The application of high-precision maps and RT systems in the test provided positioning information for the driving robot with an accuracy error of less than 2
In order to satisfy design requirements of Lane Keeping Assistance System (LKAS), a Driver Steering Override (DSO) strategy is necessary for driver’s interaction with the assistance system. The assistance system can be overridden by the strategy in case of lane change, obstacle avoidance and other emergency situations. However, evaluation and optimization of the DSO strategy for LKAS cannot easily be completed quantitatively considering driver’s acceptability. In this research, firstly subjective and objective evaluation experiment is designed. Secondly, correlations between the subjective and the objective evaluation results are established by using regression analysis. Finally, based on the correlations established previously, the optimal performance of DSO strategy is obtained by setting the desired comprehensive evaluation ratings as the optimized goal. Except for the whole process of the research, there are some details of the subjective and objective evaluation experiment design
Lane Keeping Assistance (LKA) system is a very important part in Advanced Driver Assistance Systems (ADAS). It prevents a vehicle from departing out of the lane by exerting intervention. But an inappropriate performance during LKA intervention makes driver feel uncomfortable. The intervention of LKA can be divided into 3 parts: intervention timing, intervention process and intervention ending. Many researches have studied about the intervention timing and ending, but factors during intervention process also affect driver feelings a lot, such as yaw rate and steering wheel velocity. To increase driver’s acceptance of LKA, objective and subjective tests were designed and conducted to explore important indices which are highly correlated with the driver feelings. Different kinds of LKA controller control intervention process in different ways. Therefore, it’s very important to describe the intervention process uniformly and objectively. This paper proposes 16 Characteristic Indices (CI
This SAE Recommended Practice describes motor vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for six levels of driving automation, ranging from no driving automation (level 0) to full driving automation (level 5), in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on motor vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve users of vehicles of all classes and driving automation levels (including no driving automation), as well as motorcyclists, pedal cyclists, and pedestrians
Recently, development of vehicle control system targeting Full Driving Automation (autonomous driving level 5) has advanced. Some applications of autonomous driving systems like the Lane Keeping Assist system (LKA) and Auto Lane Change system (ALC) (autonomous driving level 1-3) have been put on the market. However, the conventional system using information from front camera, it is difficult to operate in some situations. For example the road that no line, large curvature and number of lane increases or decreases. We propose an autonomous driving system using high accuracy vehicle position estimation technology and a high definition map. An LKA system calculates the target steering wheel angle based on both vehicle position information from the Global Navigation Satellite System (GNSS) and the target lane of high the definition map, according to the method of front gaze driver model. Then, the system controls steering the wheel angle by Electric Power Steering (EPS). In the case of ALC
Emerging autonomous driving technologies, with emergency navigating capabilities, necessitates innovative vehicle steering methods for operators during unanticipated scenarios. A reconfigurable “plug and play” steering system paradigm enables lateral control from any seating position in the vehicle’s interior. When required, drivers may access a stowed steering input device, establish communications with the vehicle steering subsystem, and provide direct wheel commands. Accordingly, the provision of haptic steering cues and lane keeping assistance to navigate roadways will be helpful. In this study, various steering devices have been investigated which offer reconfigurability and haptic feedback to create a flexible driving environment. A joystick and a robotic arm that offer multiple degrees of freedom were compared to a conventional steering wheel. To evaluate the concept, human test subjects interacted with the experimental system featuring a driving simulator with target hardware
This Recommended Practice provides a taxonomy for motor vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis and that range in level from no driving automation (level 0) to full driving automation (level 5). It provides detailed definitions for these six levels of driving automation in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on motor vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve users of vehicles of all classes and driving automation levels (including no driving automation), as well as motorcyclists, pedal cyclists
Mitsubishi Electric has been developing a lane keeping assist system (LKAS). This system consists of our products such as an electric power steering (EPS), a camera, and an electronic control unit (ECU) for ADAS. In this system, the camera detects a lane marker, the ECU estimates reference path and vehicle position, and calculates reference steering wheel angle, and the EPS controls a steering wheel angle based on reference steering wheel angle. In this paper, we explain the calculation method of reference steering wheel angle for path tracking control. We derive a formula of reference steering wheel angle calculation that converges lateral position deviation in desired time by using lateral position deviation change rate control on forward gaze point as path tracking control algorithm. Since the formula is obtained from the vehicle model, we can easily design a controller depending on the vehicle type, by using known vehicle specifications. In addition, we confirmed that the algorithm
RACam [1] is an Active Safety product designed and manufactured at Delphi and is part of their ADAS portfolio. It combines two sensors - Electronically Scanned RADAR and Camera in a single package. RADAR and Vision fusion data is used to realize safety critical systems such as Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Lane Departure Warning (LDW), Lane Keep Assist (LKA), Traffic Sign Recognition (TSR) and Automatic Headlight Control (AHL). Figure 1 RACam Front View. With an increase in Active Safety features in the automotive market there is also a corresponding increase in the complexity of the hardware which supports these safety features. Delphi’s hardware design for Active Safety has evolved over the years. In Delphi’s RACam product there are a number of critical components required in order to realize RADAR and Vision in a single package. RACam is also equipped with a fan and heater to improve the operating temperature range. RADAR and Camera sensors go
The purpose of this document is to provide guidance for the implementation of driver-vehicle interfaces (DVI) for intervention-type lane keeping assistance systems (LKAS), as defined by ISO 11270. LKAS provide support for safe lane keeping operations by drivers via momentary intervention in lane keeping actions, but do not automate part or all of the dynamic driving task on a sustained basis (see SAE J3016). Thus they are not classified as a driving automation system per SAE J3016 - Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, nor do they prevent possible lane or roadway departures, as drivers can always override an LKAS intervention and road conditions may be such that they cannot support an LKAS intervention (e.g., too slippery, curve to tight, lateral velocity too high, etc.). As used in this document, the term LKAS refers to lateral control driver assistance features that automatically intervene to hinder a lane departure if the
A novel speed and position dependent Lane Keeping Assistance (LKA) control strategy for heavy vehicles is proposed. This LKA system can be implemented with any torque overlay system capable of accepting external position or torque commands. The proposed algorithm tackles the problem of lane keeping in two ways from a heavy vehicle's perspective. First, it stabilizes the vehicle's lateral position by bringing it to the center of the lane and giving it the correct heading to stay there. This is done using a speed and position dependent control strategy that becomes less aggressive as the vehicle's speed increases and as it gets closer to the center of the lane. Such speed and position dependency is especially critical in heavy vehicles where unnecessary aggressive control can lead to oscillations about the lane's centerline when cruising at high speeds. Furthermore, the proposed controller allows the vehicle to negotiate the road's curvature efficiently while tracking the lane's
In this paper, switchable Lane Keeping System (LKS) and Active Lane Keeping Assist System (ALKAS) with early/late intervention criteria is proposed and developed. These two systems are commonly based on single track vehicle model and weighted lateral deviation prediction. The main difference is intervention strategy between two systems. Software In the Loop (SIL), Man In the Loop (MIL) verification are fulfilled for both systems till vehicle speed 200kph. For real vehicle verification, only LKS results shall be shown in this paper which shows small maximum lateral deviation also in road transition and vehicle speed variation. Real vehicle verification results for ALKAS shall not be shown because it is more related to steering feeling of driver
Predicting driver response to road departure and attempted recovery is a challenging but essential need for estimating the benefits of active safety systems. One promising approach has been to mathematically model the driver steering and braking inputs during departure and recovery. The objective of this paper is to compare a model developed by Volvo, Ford, and UMRTI (VFU) through the Advanced Crash Avoidance Technologies (ACAT) Program against a set of real-world departure events. These departure events, collected by Hutchinson and Kennedy, include the vehicle's off road trajectory in 256 road departure events involving passenger vehicles. The VFU-ACAT model was exercised for left side road departures onto the median of a divided highway with a speed limit of 113 kph (70 mph). At low departure angles, the VFU-ACAT model underpredicted the maximum lateral and longitudinal distances when compared to the departure events measured by Hutchinson and Kennedy. Two sets of driver parameters
Advanced Driver Assistance Systems (ADAS) for collision avoidance/mitigation have already demonstrated their benefit on vehicle safety. Often those systems have an additional functionality for comfort to assist the driver in non-critical driving. The verification of ADAS functionality using different test scenarios is currently investigated in many different projects worldwide. A harmonization of test scenarios and evaluation criteria is not yet accomplished. Often, these test scenarios focus on objective collision avoidance and not on the subjective interaction between driver and vehicle. The present study deals with the development of an experimental validation plan for the systems Automatic Cruise Control (ACC), Lane Departure Warning (LDW) and Lane Keeping Assist (LKA). Standardized driving maneuvers with two or more vehicles equipped with synchronized measurement are performed by professional test drivers. For this purpose selected public roads are used, and the different
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