Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.

Current Advances of CO Sensing Based on Low Dimensional Materials.

Carbon monoxide (CO) is a harmful gas with significant impacts on human health and the environment. Its timely detection, especially in the event of thermal runaway in automotive lithium batteries, is crucial to prevent casualties. This paper reviews the progress in the development of efficient, sensitive, and reliable CO sensors, focusing on electrochemical, optical, and resistive sensing materials. Low-dimensional materials have a large specific surface area, providing an abundant number of active sites, which has drawn extensive attention from researchers. According to the different sensor signals, we categorized these sensors into electrical and optical signal sensors. We hope that by systematically introducing the sensing mechanism and sensing performance of these two kinds of sensors, appropriate CO sensors can be developed in different application scenarios so as to realize early warning and monitoring to the maximum extent, reduce industrial losses, and ensure the life and health of personnel.

Sequence Decision Transformer for Adaptive Traffic Signal Control.

Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement learning (DRL) have further enhanced ATSC's capabilities. This paper introduces a novel DRL-based ATSC approach named the Sequence Decision Transformer (SDT), employing DRL enhanced with attention mechanisms and leveraging the robust capabilities of sequence decision models, akin to those used in advanced natural language processing, adapted here to tackle the complexities of urban traffic management. Firstly, the ATSC problem is modeled as a Markov Decision Process (MDP), with the observation space, action space, and reward function carefully defined. Subsequently, we propose SDT, specifically tailored to solve the MDP problem. The SDT model uses a transformer-based architecture with an encoder and decoder in an actor-critic structure. The encoder processes observations and outputs, both encoded data for the decoder, and value estimates for parameter updates. The decoder, as the policy network, outputs the agent's actions. Proximal Policy Optimization (PPO) is used to update the policy network based on historical data, enhancing decision-making in ATSC. This approach significantly reduces training times, effectively manages larger observation spaces, captures dynamic changes in traffic conditions more accurately, and enhances traffic throughput. Finally, the SDT model is trained and evaluated in synthetic scenarios by comparing the number of vehicles, average speed, and queue length against three baselines, including PPO, a DQN tailored for ATSC, and FRAP, a state-of-the-art ATSC algorithm. SDT shows improvements of 26.8%, 150%, and 21.7% over traditional ATSC algorithms, and 18%, 30%, and 15.6% over the FRAP. This research underscores the potential of integrating Large Language Models (LLMs) with DRL for traffic management, offering a promising solution to urban congestion.

Constraint-Guided Behavior Transformer for Centralized Coordination of Connected and Automated Vehicles at Intersections.

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort.

Multi-Attack Intrusion Detection for In-Vehicle CAN-FD Messages.

As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively.

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification.

The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship.

Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm.

Big data already covers intelligent vehicles and is driving the autonomous driving industry's transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.

A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive.

Brain-like intelligent decision-making is a prevailing trend in today's world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers' driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

De-Skewing LiDAR Scan for Refinement of Local Mapping.

Simultaneous localization and mapping have become a basic requirement for most automatic moving robots. However, the LiDAR scan suffers from skewing caused by high-acceleration motion that reduces the precision in the latter mapping or classification process. In this study, we improve the quality of mapping results through a de-skewing LiDAR scan. By integrating high-sampling frequency IMU (inertial measurement unit) measurements and establishing a motion equation for time, we can get the pose of every point in this scan's frame. Then, all points in this scan are corrected and transformed into the frame of the first point. We expand the scope of optimization range from the current scan to a local range of point clouds that not only considers the motion of LiDAR but also takes advantage of the neighboring LiDAR scans. Finally, we validate the performance of our algorithm in indoor and outdoor experiments to compare the mapping results before and after de-skewing. Experimental results show that our method smooths the scan skewing on each channel and improves the mapping accuracy.

Bio-Inspired Electrodes with Rational Spatiotemporal Management for Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) are currently the predominant energy storage power source. However, the urgent issues of enhancing electrochemical performance, prolonging lifetime, preventing thermal runaway-caused fires, and intelligent application are obstacles to their applications. Herein, bio-inspired electrodes owning spatiotemporal management of self-healing, fast ion transport, fire-extinguishing, thermoresponsive switching, recycling, and flexibility are overviewed comprehensively, showing great promising potentials in practical application due to the significantly enhanced durability and thermal safety of LIBs. Taking advantage of the self-healing core-shell structures, binders, capsules, or liquid metal alloys, these electrodes can maintain the mechanical integrity during the lithiation-delithiation cycling. After the incorporation of fire-extinguishing binders, current collectors, or capsules, flame retardants can be released spatiotemporally during thermal runaway to ensure safety. Thermoresponsive switching electrodes are also constructed though adding thermally responsive components, which can rapidly switch LIB off under abnormal conditions and resume their functions quickly when normal operating conditions return. Finally, the challenges of bio-inspired electrode designs are presented to optimize the spatiotemporal management of LIBs. It is anticipated that the proposed electrodes with spatiotemporal management will not only promote industrial application, but also strengthen the fundamental research of bionics in energy storage.

Numerical investigation of thermal soak within engine bay using lattice Boltzmann method.

A large amount of heat accumulates in the engine bay for a short time after the engine runs at high load and shuts down, that will lead to thermal damage and thermal fatigue caused by the temperature rise of some heat sensitive components. This paper uses an aero-thermal coupling approach to study the heat transfer problem in the engine bay of an SUV model under thermal soak conditions. Due to the transient characteristics of the heat transfer process, the natural transient CFD software developed based on the LBM method is used to study the engine bay heat transfer during the 400 s key-off soak process. The analysis reveals that convection and radiation are the main heat transfer modes in the early stage of hot immersion (0-120 s), and conduction only makes a significant contribution in contact with high temperature sources. The radiation and convection are the key contributors to heat transfer processes of engine bay during soak, but the efficiency of radiation heat transfer decreases with the increase of time, whereas the efficiency of convection heat transfer is not always reduced, it will increase and then decrease with the increase of time. The coupling method established can predict the thermal state in the engine bay well, and is in good agreement with the experimental results. The results show that the error in the engine coolant temperature is less than 1 °C, and the error in the temperature of the heat-sensitive components is less than 5 °C. Finally, the potential risks of thermal damage and thermal fatigue states were assessed, providing an important reference for the control design of cooling fan running time after key-off.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Bionic Capsule Lithium-Ion Battery Anodes for Efficiently Inhibiting Volume Expansion.

Magnetite (Fe3O4) has a large theoretical reversible capacity and rich Earth abundance, making it a promising anode material for LIBs. However, it suffers from drastic volume changes during the lithiation process, which lead to poor cycle stability and low-rate performance. Hence, there is an urgent need for a solution to address the issue of volume expansion. Taking inspiration from how glycophyte cells mitigate excessive water uptake/loss through their cell wall to preserve the structural integrity of cells, we designed Fe3O4@PMMA multi-core capsules by microemulsion polymerization as a kind of anode materials, also proposed a new evaluation method for real-time repair effect of the battery capacity. The Fe3O4@PMMA anode shows a high reversible specific capacity (858.0 mAh g-1 at 0.1 C after 300 cycles) and an excellent cycle stability (450.99 mAh g-1 at 0.5 C after 450 cycles). Furthermore, the LiNi0.8Co0.1Mn0.1O2/Fe3O4@PMMA pouch cells exhibit a stable capacity (200.6 mAh) and high-capacity retention rate (95.5 %) after 450 cycles at 0.5 C. Compared to the original battery, the capacity repair rate of this battery is as high as 93.4 %. This kind of bionic capsules provide an innovative solution for improving the electrochemical performance of Fe3O4 anodes to promote their industrial applications.

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20 mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20 mph. Results: In a lateral movement, the excursions of the dummy head were 100 mm and 70 mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150 mm and 180 mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

The novel, orally bioavailable HSP90 inhibitor NVP-HSP990 induces cell cycle arrest and apoptosis in multiple myeloma cells and acts synergistically with melphalan by increased cleavage of caspases.

Heat shock protein 90 (HSP90) binds and stabilizes numerous proteins and kinases essential for myeloma cell survival and proliferation. We and others have recently demonstrated that inhibition of HSP90 by small molecular mass inhibitors induces cell death in multiple myeloma (MM). However, some of the HSP90 inhibitors involved in early clinical trials have shown limited antitumor activity and unfavorable toxicity profiles. Here, we analyzed the effects of the novel, orally bioavailable HSP90 inhibitor NVP-HSP990 on MM cell proliferation and survival. The inhibitor led to a significant reduction in myeloma cell viability and induced G2 cell cycle arrest, degradation of caspase-8 and caspase-3, and induction of apoptosis. Inhibition of the HSP90 ATPase activity was accompanied by the degradation of MM phospho-Akt and phospho-ERK1/2 and upregulation of Hsp70. Exposure of MM cells to a combination of NVP-HSP990 and either melphalan or histone deacetylase (HDAC) inhibitors caused synergistic inhibition of viability, increased induction of apoptosis, and was able to overcome the primary resistance of the cell line RPMI-8226 to HSP90 inhibition. Combined incubation with melphalan and NVP-HSP990 led to synergistically increased cleavage of caspase-2, caspase-9, and caspase-3. These data demonstrate promising activity for NVP-HSP990 as single agent or combination treatment in MM and provide a rationale for clinical trials.

Bioinspired Thermal Runaway Retardant Capsules for Improved Safety and Electrochemical Performance in Lithium-Ion Batteries.

Vigorous development of electric vehicles is one way to achieve global carbon reduction goals. However, fires caused by thermal runaway of the power battery has seriously hindered large-scale development. Adding thermal runaway retardants (TRRs) to electrolytes is an effective way to improve battery safety, but it often reduces electrochemical performance. Therefore, it is difficult to apply in practice. TRR encapsulation is inspired by the core-shell structures such as cells, seeds, eggs, and fruits in nature. In these natural products, the shell isolates the core from the outside, and has to break as needed to expose the core, such as in seed germination, chicken hatching, etc. Similarly, TRR encapsulation avoids direct contact between the TRR and the electrolyte, so it does not affect the electrochemical performance of the battery during normal operation. When lithium-ion battery (LIB) thermal runaway occurs, the capsules release TRRs to slow down and even prevent further thermal runaway. This review aims to summarize the fundamentals of bioinspired TRR capsules and highlight recent key progress in LIBs with TRR capsules to improve LIB safety. It is anticipated that this review will inspire further improvement in battery safety, especially for emerging LIBs with high-electrochemical performance.

Fire boundaries of lithium-ion cell eruption gases caused by thermal runaway.

Lithium-ion batteries are applied in electric vehicles to mitigate climate change. However, their practical applications are impeded by poor safety performance owing mainly to the cell eruption gas (CEG) fire triangle. Here, we report quantitatively the three fire boundaries corresponding to the CEG fire triangle of four types of mainstream cells with the state of charge (SOC) values ranging from 0% to 143% based on 29 thermal runaway tests conducted in an inert atmosphere in open literature. Controlling the SOC and/or selecting a reasonable cell type can alter the minimum CEG and oxygen concentrations required for ignition, thereby changing the probability of a battery fire. The ignition temperature varies greatly according to the type of ignition source type. Temperature and ignition source type play a leading role in the ignition mode. Breaking any fire boundary will stop the ignition of CEG, thus significantly improving the battery safety performance.

Investigation of the instinctive reaction of human drivers in social dilemma based on the use of a driving simulator and a questionnaire survey.

Objective：The moral and ethical issue is a great challenge to the development of autonomous vehicles. There may be distinctions between the choices made by an observer and a participant. The paper is designed to investigate whether drivers will sacrifice the fewest people to save more people in social dilemma, and whether human drivers would give priority to protecting pedestrians or self-protection in an emergency.Methodology: The experiment was conducted with a total of 50 participants assigned to three groups. Three experimental scenarios were designed and each of them contained a social dilemma. A driving simulator was used in this study to explore the choices of human drivers in social dilemma. In addition, the simulator results were compared with those of questionnaire survey.Result: In study 1, 73% of 22 participants swerved into the right lane to hit only one pedestrian for the safety of other five. In study 2 and 3, more participants chose to hit the barrier to protect the pedestrian.Conclusion: A conclusion can be drawn from the second and third group of experiments that most drivers consider not only their own safety, but the safety of pedestrians. Most of the participants intended to minimize the total amount of harm in social dilemma. The choice of crashing into barriers to protect a pedestrian can also be seen as a way to minimize the total amount of harm.

Musculoskeletal computational analysis on muscle mechanical characteristics of drivers' lumbar vertebras and legs in different sitting postures.

Using computer-aided engineering (CAE) in the concept design stage of automobiles has become a hotspot in human factor engineering research. Based on human musculoskeletal biomechanical computational software, a seated human-body musculoskeletal model was built to describe the natural sitting posture of a driver. The interaction between the driver and car in various combinations of seat-pan/back-rest inclination angles was analyzed using an inverse-dynamics approach. In order to find out the "most comfortable" driving posture of the seat-pan/back-rest, the effect of seat-pan/back-rest inclination angles on the muscle activity degree, and the intradiscal L4-L5 compression force were investigated. The results showed that a much larger back-rest inclination angle, approximately 15°, and a slight backward seat-pan, about 7°, may relieve muscle fatigue and provide more comfort while driving. Subsequently, according to the findings above, a preliminary driving-comfort function was constructed.

Simulator study of young driver's instinctive response of lower extremity to a collision.

OBJECTIVE: A driver's instinctive response of the lower extremity in braking movement consists of two parts, including reaction time and braking reaction behavior. It is critical to consider these two components when conducting studies concerning driver's brake movement intention and injury analysis. The purposes of this study were to investigate the driver reaction time to an oncoming collision and muscle activation of lower extremity muscles at the collision moment. The ultimate goal is to provide data that aid in both the optimization of intervention time of an active safety system and the improvement of precise protection performance of a passive safety system. METHOD: A simulated collision scene was constructed in a driving simulator, and 40 young volunteers (20 male and 20 female) were recruited for tests. Vehicle control parameters and electromyography characteristics of eight muscles of the lower extremity were recorded. The driver reaction time was divided into pre-motor time (PMT) and muscle activation time (MAT). Muscle activation level (ACOL) at the collision moment was calculated and analysed. RESULTS: PMT was shortest for the tibialis anterior (TA) muscle (243∼317 ms for male and 278∼438 ms for female). Average MAT of the TA ranged from 28-55 ms. ACOL was large (5∼31% for male and 5∼23% for female) at 50 km/h, but small (<12%) at 100 km/h. ACOL of the gluteus maximus was smallest (<3%) in the 25 and 100 km/h tests. ACOL of RF of men was significantly smaller than that of women at different speeds. CONCLUSIONS: Ankle dorsiflexion is firstly activated at the beginning of the emergency brake motion. Males showed stronger reaction ability than females, as suggested by male's shorter PMT. The detection of driver's brake intention is upwards of 55ms sooner after introducing the electromyography. Muscle activation of the lower extremity is an important factor for 50 km/h collision injury analysis. For higher speed collisions, this might not be a major factor. The activations of certain muscles may be ignored for crash injury analysis at certain speeds, such as gluteus maximus at 25 or 100 km/h. Furthermore, the activation of certain muscles should be differentiated between males and females during injury analysis.