Search Results (23)

Advanced driver assistance system (ADAS) technologies are key to improving road safety and promoting innovation in the automotive sector. The approval and analysis of ADAS systems, especially automatic emergency braking (AEB) tests, require complex procedures and in-depth data management. This work presents innovative tools developed to facilitate the post-processing of ADAS AEB test data, created in collaboration with Nardò Technical Center. The tool, called Autonomous Code Generation Intelligence (ACGI), introduces an intuitive and intelligent user interface that helps analyze and interpret ADAS test approval regulations. ACGI automates the generation of code sections within a data analytics framework, streamlining the compliance process and significantly reducing the time and programming skills required. This tool allows engineers to focus on high-value tasks, improving overall process efficiency. To achieve this objective, the tool encodes the DAART code framework (Data Analysis and Automated Report Tool) which allows users to carry out real post-processing of the tests conducted on the track. The results demonstrate that both tools simplify and automate critical steps in the ADAS automatic emergency braking test data analysis process. In fact, the tool not only improves the accuracy and efficiency of the analyses but also offers a high degree of customization, making it a flexible and adaptable tool to meet the specific needs of users. In future developments, ACGI could be extended to cover additional ADAS tests and could be equipped with artificial intelligence to suggest configurations based on new regulations. Full article

The contribution of this paper is to present an automatic emergency braking (AEB) optimized algorithm based on time to collision (TTC) and a professional driver fitting (PDF) braking pattern. When the TTC value is less than the given threshold, the PDF control algorithm will be started, and vice versa. According to the standard test scenarios for passenger cars and commercial vehicles, the simulation analysis on the AEB systems using four different control algorithms, namely TTC, quadratic curve deceleration, PDF and proposed optimized control algorithm, is conducted, respectively. The results show that the proposed optimization algorithm can both meet the standard requirements and improve the ride comfort. While ensuring collision avoidance with the preceding vehicle, the control algorithm proposed in this study offers better braking comfort compared to the TTC algorithm and the quadratic curve deceleration algorithm. Additionally, it provides a more appropriate stopping distance compared to the PDF algorithm. Full article

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles’ bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird’s eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively. Full article

The increasing demand for efficient and cost-effective transportation solutions has led to the exploration of unconventional modes of transportation, such as ziplines. This paper presents the development of an electric prototype for a trolley that can be used for the simultaneous transport of goods and people on ziplines. The prototype is designed with a modular system that allows for easy customization based on the cargo’s weight and size. Two lightweight Maxon motors have been integrated for traction purposes with two Maxon inverters and a low-voltage swappable battery pack. The trolley’s chassis is made of lightweight materials, such as aluminum, making it highly maneuverable and capable of traveling at high speeds. The lightweight permits the operators to detach the trolley from the zipline when needed. The prototype’s traction and braking systems are controlled through a user-friendly interface, making it easy to operate, and with the possibility of a robust and automatic routine for goods transport. In this article, we present the simulation for the design and testing of the prototype, as well as its potential applications in various industries, such as mining, agriculture, and emergency services. Our results show that the prototype is a viable solution for zipline-based transportation, with high efficiency and performance standards. Further research and development are being conducted to optimize the prototype’s performance and expand its applications. Full article

Existing testing methods for Automatic Emergency Braking (AEB) systems mostly rely on standard-based qualitative analysis of specific scenarios, with a focus on whether collisions occur. To explore scenarios beyond the standard conduct, a comprehensive testing model construction and analysis, and provide a more quantitative evaluation of AEB performance, this study extracted three typical hazardous driving scenarios from the KITTI (The Automated Driving dataset was created by the Karlsruhe Institute of Technology in Germany and the Toyota Institute of Technology in the United States) naturalistic driving dataset using kinematic data. A DME (Data Missing Estimation) scene construction method was proposed, and these scenarios were simulated and reconstructed in PRESCAN (PRESCAN is an automotive simulation software owned by Siemens, Munich, Germany). A C-AEB (Curve-Automatic Emergency Braking) testing model was developed and tested based on simulations. Finally, a BCEM (Boundary collision evaluation model) was proposed to quantitatively evaluate AEB performance. The focus of the analysis was on the identified cornering scenario A (severely failed AEB scenario). A C-AEB testing model was constructed based on the DME scene construction method for this cornering AEB failure scenario, and it was evaluated using the BCEM. The study found that the average performance degradation rate (performance degradation rate refers to the ratio of AEB performance in the current scenario compared to the standard straightaway test) of the AEB system in this cornering scenario reached 75.44%, with a maximum performance degradation rate of 89.47%. It was also discovered that the severe failure of AEB in this cornering scenario was mainly caused by sensor system perception defects and limitations of traditional AEB algorithms. This fully demonstrates the effectiveness of our testing and evaluation methodology. Full article

In this paper, we propose a method for the generation of simulated test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtually simulated scenarios and functional scenario libraries for autonomous driving, which can be used for the simulated verification of different ADAS functions. Firstly, the operational design domain (ODD) of a functional scenario is selected, and the weight values of the ODD elements are calculated. Then, a combination test algorithm based on parameter weights is improved to generate virtually simulated autonomous driving test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm. Then, the traffic participant elements in each test case are sampled and clustered so as to obtain hazard-specific scenarios. Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard-specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method. Full article

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible. Full article

The operation of the automatic emergency braking (AEB) system may lead to a significant increase in lateral offset of vehicles in curved road conditions, which can pose a potential risk of collisions with vehicles in adjacent lanes or road edges. In order to address this issue, this study proposes an integrated longitudinal and lateral control strategy for collision avoidance during emergency braking, which utilizes a control algorithm based on Time to Collision (TTC) for longitudinal control and a control algorithm based on yaw angle and preview point lateral deviation for lateral control. On one hand, the AEB system facilitates proactive longitudinal intervention to prevent collisions in the forward direction. On the other hand, the Lane Keeping Assist (LKA) system allows for lateral intervention, reducing the lateral offset of the vehicle during braking. To evaluate the effectiveness of this integrated control strategy, a collaborative simulation model involving Matlab/Simulink, PreScan, and CarSim is constructed. Under typical curved road conditions, comparative simulations are conducted among three different control systems: ➀ AEB control system alone; ➁ independent control system of AEB and LKA; and ➂ integrated control system of AEB and LKA. The results indicate that although all three control systems are effective in preventing longitudinal rear-end collisions, the integrated control system outperforms the other two control systems significantly in suppressing the vehicle’s lateral offset. In the scenario with a curve radius of 60 m and an initial vehicle speed of 60 km/h, System ➀ exhibits a lateral offset from the lane centerline reaching up to 1.72 m. In contrast, Systems ➁ and ➂ demonstrate significant improvements with lateral offsets of 0.29 m and 0.21 m, respectively. Full article

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort. Full article

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method. Full article

As an advanced driver assistance system, automatic emergency braking (AEB) can effectively reduce accidents by using high-precision and high-coverage sensors. In particular, it has a significant advantage in reducing front-end collisions and rear-end accidents. Unfortunately, avoiding side collisions is a challenging problem for AEB. To tackle these challenges, we propose active seat belt pretensioning on driver injury in vehicles equipped with AEB in unavoidable side crashes. Firstly, records of impact cases from China’s National Automobile Accident In-Depth Investigation System were used to investigate a scenario in which a vehicle is impacted by an oncoming car after the vehicle’s AEB system is triggered. The scenario was created using PreScan software. Then, the simulated vehicles in the side impact were devised using a finite element model of the Toyota Yaris and a moving barrier. These were constructed in HyperMesh software along with models of the driver’s side seatbelt, side airbag, and side curtain airbag. Moreover, the models were verified, and driver out-of-position instances and injuries were evaluated in simulations with different AEB intensities up to 0.7 g for three typical side impact angles. Last but not least, the optimal combination of seatbelt pretensioning and the timing thereof for minimizing driver injury at each side impact angle was identified using orthogonal tests; immediate (at 0 ms) pretensioning at 80 N was applied. Our experiments show that our active seatbelt with the above parameters reduced the weighted injury criterion by 5.94%, 22.05%, and 20.37% at impact angles of 90°, 105°, and 120°, respectively, compared to that of a conventional seatbelt. The results of the experiment can be used as a reference to appropriately set the collision parameters of active seat belts for vehicles with AEB. Full article

In order to improve the active safety of vehicles, the control strategy of the vehicle automatic emergency braking system is studied. The hierarchical control idea is used to model the control strategy. The upper controller is a collision time model for the decision-making of vehicle braking deceleration, and the collision time threshold is determined under the condition of considering comfort. According to the braking deceleration output by the upper controller, the lower controller controls the vehicle by adjusting the throttle opening and braking pipeline pressure through PID control. Based on the typical test conditions of C-NCAP, a joint simulation test of CarSim and Matlab/Simulink for hierarchical control strategy is carried out. In order to achieve further verification, several groups of test conditions are conducted, and finally its effectiveness is verified, which can ensure the safety of drivers. Full article

The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car. Full article

Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively. Full article

Automatic emergency braking systems (AEBS) significantly improve the active safety performance of commercial vehicles, but their effectiveness is affected by the vehicle’s driving conditions, which mainly include the vehicle load and road conditions. In order to improve the adaptability of the AEBS, an AEBS control strategy with adaptive driving conditions was proposed and validated using a simulation and experimentation. This AEBS control strategy was designed based on an estimation of the vehicle mass, the center of gravity position, road grade, and the tire-road friction coefficient. In the simulation and experimental verification, the braking deceleration and braking distance under different driving conditions were compared. The results show that the AEBS control strategy proposed in this paper can avoid collisions in all test scenarios and maintain a parking spacing of approximately 5 m. In an extreme test scenario with a full load and low tire–road friction, as compared with the fixed threshold control strategy, the warning can be issued 0.2 s earlier and the maximum intensity braking can be carried out 0.5 s earlier. Full article