在论文中，Waymo case by case 地概述了其他人类驾驶员的 “违反道路规则”
是如何导致 “严重” 碰撞的。
automated vehicles (AVs)
automated driving system (ADS)
operational design domain (ODD)
这份报告总结了 Waymo 在凤凰城 (Phoenix) 测试区域的 610
万英里的自动驾驶测试数据，包括有车上安全员监管 ( trained operator behind
the steering wheel) 的自动驾驶，和 65000 英里的没有安全驾驶员无人驾驶
(driverless operation)。 > The data presented in this paper
represents more than 6.1 million miles of automated driving in the
Phoenix, Arizona metropolitan area, including operations with a trained
operator behind the steering wheel from calendar year 2019 and 65,000
miles of driverless operation without a human behind the steering wheel
from 2019 and the first nine months of 2020.
报告中承认，这段测试期间 Waymo 自动驾驶汽车卷入了 18
> There were 47 contact events that occurred over this time period,
consisting of 18 actual and 29 simulated contact events, none of which
would be expected to result in severe or life-threatening
> Nearly all the events involved one or more road rule violations or
other errors by a human driver or road user. > The presence of
collisions that resulted from challenging situations induced by other
drivers serves as a reminder of the limits of AV collision avoidance as
long as AVs share roadways with human drivers.
> The long-term contributions of this paper are not only the events
and mileages shared, but the example set by publicly sharing this type
of safety information.
(acceptance)。 > The purpose of this paper is to make available
relevant data to promote awareness and discussions that ultimately
foster greater public confidence in AVs.
Public Road Testing
> In order to perform initial public road testing of AVs in a safe
and responsible manner, trained vehicle operators are seated in the
driver’s seat and can take over the driving task at any time.
> Counterfactual disengagement simulation is used to represent the
predicted vehicle response for a brief period (seconds) after
disengagement, and the simulation outcome provides insight into what
could have happened had the trained operator not intervened. > MC:
反事实的接管仿真可以独立使用 (individually) 也可以聚合使用 (in
aggregate)。 > The outcomes of counterfactual disengagement
simulations are used both individually and in aggregate.
Individual counterfactual disengagement simulation: If the simulation
outcome reveals an opportunity to improve the behavior of the ADS, then
the simulation is used to develop and test changes to software
algorithms. The disengagement event is also added to a library of
scenarios, so that future software can be tested against the scenario.
聚合使用：评估 AVs 的路测表现。 > At an aggregate level, Waymo
uses results from counterfactual disengagement simulations to produce
metrics relevant to the AV’s on-road performance.
软件进化带来的仿真不可复现性问题 > Waymo’s models will
continue to evolve, and even for these brief simulations, future models
may result in different simulated outcomes.
Aims and Contributions of
报告包括在 610 万英里测试中的事故总计和事故描述
500 多年的驾驶里程。 > This paper includes safety data in the form of
event counts and event descriptions from over 6.1 million miles of
driving conducted in the Waymo Driver’s driverless ODD. This mileage
figure represents over 500 years of driving for the average U.S.
> For these miles, this paper provides information regarding
every actual contact event that vehicles were involved in during
driverless operation with and without trained operators, as well
events in which the vehicle’s trained operator disengaged and
subsequent counterfactual simulation resulted in any contact between the
AV and the other agent, had the disengagement not occurred
Waymo 自动驾驶软件的最高速度是 45 英里每小时，换算成公里数是
The ODD includes roadways with speed limits up to and including 45 miles
per hour. Driverless operations occur at all times day and night, except
during inclement weather including heavy rain and dust storms.
报告分享的数据来源于两方面： - 无人驾驶（Driverless
driving system) 控制车辆。 > Driverless operation, in which the
automated driving system controls the vehicle for the entire trip
without a human driver behind the wheel or otherwise being available to
assume any part of the driving task.
> MC: 这种模式下，在 2019 年初到 2020 年九月底的测试里程是 6.5
由驾驶员的自动驾驶（Self-driving with trained
> Self-driving with trained operators , in which the automated
driving system controls the vehicle but there is a trained vehicle
operator in the driver’s seat who can disengage and take over the
> MC: 这种模式下，2019 年全年的测试里程是 6.1
(controlling for potential seasonality
Data from Actual
Collisions and Minor Contacts
数据包括自动驾驶 (self-driving with trained operators mode)
或无人驾驶 (driverless mode) 模式下每一个真实碰撞 (actual collision)
和小事故 (minor contact event)，甚至包括行人撞上静止自车的事故。 >
This definition encompasses not only every severity of collision, but
also events such as a pedestrian walking into the side of the stationary
Counterfactual (“What If”) Simulation
the AV motion post-disengagement
接管后的仿真 (post-disengage simulation) 第一步，就是仿真自车的表现
(AV’s counterfactual post-disengage
motion)。这样的仿真比较简单，容易快速实现。 > The first step in
post-disengage simulation is therefore to simulate the AV’s
counterfactual post-disengage motion. > This is performed by
providing a simulation running Waymo self-driving software with the AV’s
pre-disengage position, attitude, velocity, and acceleration along with
the AV’s recorded sensor observations and simulating the response of the
software and resulting motion of the Waymo vehicle.
positions) 意味着有潜在的碰撞 (potential collision)。 > Overlapping
positions indicate a potential collision. After the AV’s post-disengage
motion is simulated, a check is performed to determine if the simulated
positions of the AV overlap at any point with the recorded positions of
(modeling the behavior of other
agents)。这是比较困难的，需要有比较丰富的模型和交互设计。 > This may
not be realistic in cases where the other agents would
likely have responded differently to the AV’s counterfactual simulated
motion than they did to the AV’s actual post-disengage motion. In such
cases, further simulation is required.
(conflict-avoidance) 或避障行为 (collision-avoidance) 是可行的做法。
> While modeling agent behavior over long periods of time is
challenging, understanding plausible conflict-avoidance or
collision-avoidance behavior over the short time horizon following a
disengagement is a more feasible task.
Waymo 使用人类避障行为模型 (human collision avoidance behavior
(the space of plausible reactions)。 > Waymo expresses short-term
agent responses using human collision avoidance behavior models. >
These models aim to capture the responses of human drivers,
motorcyclists, cyclists, and pedestrians to collision avoidance
situations, such as braking by a lead vehicle or being cut-off by
another agent who fails to yield right-of-way. > Because only the
agent’s short-term response needs to be modeled, the space of plausible
reactions to such stimuli can be defined using a discrete set of
factors such as response times to specific inputs and brake or
AV 行为时，使用一个范围来表示可能的人类驾驶表现 (a broad spectrum of
potential human driving performance).
为了透明度和简单性，本文使用确定的模型 (deterministic model)
来对一个给定的输入产生给定的输出。 > Waymo considers a broad spectrum
of potential human driving performance in developing and evaluating the
AV, but for transparency and simplicity, the results reported in this
paper are based on deterministic models that generate a single response
to a given input.
> Other methods can be used to capture a range of possible human
responses, such as probabilistic counterfactual outcomes, but they are
(road user behavior modelling frameworks)，并基于人类自然的避碰模型
(naturalistic human collision) 和差点碰撞的数据 (near-collision)
来校正。 > Waymo’s proprietary human collsion avoidance behavior
models are based on existing road user behavior modelling frameworks and
calibrated using naturalistic human collision and near-collision
stimuli)，使用不同的模型。 > The agent’s response is further
constrained by human braking and steering limitations. Waymo uses
different models for different types of agents, including heavy
trucks, pedestrians, and cyclists, and for different stimuli
such as a forward agent braking or an agent emerging from behind an
(human collision avoidance behavior models)。 > Human collision
avoidance behavior models are employed for disengagements in which there
is overlap between the simulated post-disengage trajectory of the AV and
the actual post-disengage trajectory of another agent.
> In these cases, instead of using the agent’s recorded
post-disengage trajectory, the post-disengage trajectory of the other
agent is determined by applying the relevant human collision avoidance
Contact analysis of
99% 的接管没有仿真的接触 (simulated contact) 发生。 > Our simulation
analysis indicates that disengagements would rarely result in contact.
In fact, in more than 99.9% of disengagements, no simulated contact is
found to occur.
> MC: 因为 Waymo
如何碰撞发生，那么如何确定碰撞的严重程度 (event severity) 呢？Waymo
根据碰撞障碍物的类型 (collision object)，相对速度 (impact velocity)
和碰撞位置 (impact geometry) 来确定可能的伤害程度 (likelihood of
injury)。Waymo 使用国家碰撞数据库 (national crash databases)
来为事故严重程度分级 (event severity
category)，事件的统计等级分为预计无伤害 (S0) 到可能的严重伤害 (S1、S2 和
S3)。 > This determination categorizes collisions based on likelihood
of injury and is based on the collision object (e.g., other vehicles,
static objects, or vulnerable road users such as pedestrians or
cyclists), impact velocity, and impact geometry.
> Waymo’s methods for determining event severity category are
developed using national crash databases and are periodically refined to
reflect updated data.
Results: Collisions and
上表中的碰撞类型列分类 (collision typology)
是根据美国国家机构的专业分类 (using the Manner of Collision categories
from National Highway Traffic Safety Administration (NHTSA) collision
databases such as the Fatality Analysis Reporting System);
碰撞程度行分类是基于国际标准 ISO 26262
来评估碰撞的严重程度，从没有伤害的 S0 (no injury expected)
到有关键伤害的 S3 (possible critical injuries
expected)，碰撞的伤害逐渐增大。 > categorized in rows according to
their collision typology using the Manner of Collision
categories from National Highway Traffic Safety Administration (NHTSA)
collision databases such as the Fatality Analysis Reporting
> columns categorized by estimated event severity using the ISO 26262
severity classes: S0, S1, S2, and S3, ranging from no injury expected
(S0) to possible critical injuries expected (S3). * MC:
Waymo 报告中的碰撞事故中，没有 S2 或 S3 级别事故发生，最严重的事故是
S1 级别 (airbag-deployment-level)，有 3 次保护气囊弹出。 > There were
no actual or predicted S2 or S3 events. One actual event involved
deployment of another vehicle’s frontal airbags and the Waymo vehicle’s
> Comparison between these human collision statistics and Waymo event
counts provides insight into the Waymo Driver’s opportunity for reducing
injuries and fatalities due to collisions.
In total, the Waymo vehicle was involved in 20 events involving
contact with another object and experienced 27 disengagements that
resulted in contact in post-disengagement simulation, for a total of 47
events (actual and simulated).
报告以图片形式 (diagrams have been provided)，重点关注了 3
个和弱势交通群体的交互事故，以及 8 个有安全气囊弹出的严重事故。 >
Specifically, diagrams have been provided for every actual or simulated
event in which a pedestrian or cyclist was involved (three events) and
every event with actual or simulated airbag deployment for any involved
vehicle (eight events).
Single Vehicle Events
根据 Manner of Collision 的分类标准，交通事故可以分为单车事故 (single
vehicle events) 和多车事故 (multiple-vehicle events)。 > The Manner
of Collision categories within the NHTSA crash database can be broadly
classified as either single vehicle events, which involve a single
motorized vehicle in transport, or multiple-vehicle events, which
involve the impact of at least two motorized vehicles in transport.
Waymo 自动驾驶车辆没有发生偏离车道 (road departure)
和撞上行人的单车事故 (struck a pedestrian or
cyclist)；而这类事故在人类驾驶数据中占比约为 60%。 > The Waymo Driver
did not have any events (actual or simulated) in this data that involved
road departure, contact with the roadway environment/infrastructure or
other fixed objects, or rollover. There were also no collisions (actual
or simulated) in which the Waymo Driver struck a pedestrian or
Waymo 在减速或者静止时，被行人或滑板车从右侧撞上。 > In each
instance, the Waymo Driver decelerated and stopped, and a pedestrian or
cyclist made contact with the right side of the
stationary Waymo vehicle while the pedestrian or cyclist was traveling
at low speeds. *
Events: Reversing Reversing
倒车碰撞 (reversing collisions)
事故经常发生在停车场，很少出现在交警报告数据库中。 > Reversing
collisions (e.g., rear-to-front, rear-to-side, rear-to-rear) are usually
associated with parking lot events or occur on local ( ≤ 25 mph)
roadways and do not frequently appear in databases of police-reported
In both scenarios, the Waymo vehicle was stopped or traveling
forward at low speed and the other vehicle was reversing at a
speed of less than 3 mph at the moment of contact to the side of the
Vehicle Events: Same Direction Sideswipe
同方向剐蹭 (sideswipe) 主要发生在变道 (lane changing) 和并道 (lane
merging) 行为时。 > These events are typically experienced during
lane changing or merging maneuvers.
> The Waymo Driver was involved in ten simulated
same direction sideswipe collisions.
vehicle changing lanes, Waymo vehicle straight
The other vehicle changed lanes into the area
occupied by the Waymo vehicle, which resulted in simulated or actual
vehicle straight, Waymo vehicle changing lanes
In both of these simulations, the Waymo Driver made a lateral
movement in front of a vehicle traveling straight in an
Vehicle Events: Head-on or Opposite Direction Sideswipe
对头碰撞极易发生严重的交通事故 (high severity)。 > Head-on
collisions have the potential for high severity outcomes.
(impaired) 或疲劳驾驶 (fatigued) 等异常情况。 > The absence
of simulated collision avoidance movement by the other vehicle
reflects our assumption based on driving behavior and circumstances that
the other driver was significantly impaired or fatigued.
如何区别示意图中哪部分是真实，哪部分是仿真的：actual collisions are
represented in color, while simulated ones feature a black and white
background. Solid trajectory lines represent those observed in real
life, while dashed trajectories and shaded poses represent simulated
conditions. Diagrams are intended for visual reference only, and are not
drawn to scale.
Multiple Vehicle Events: Rear
追尾碰撞 (rear end collisions) 是最常见的人类驾驶员的碰撞行为。 >
Rear End collisions are the most common collision type in human-driven
> The Waymo Driver was involved in fourteen actual and two simulated
rear end collisions
end struck event group, Waymo vehicle stopped or gradually decelerating
for traffic controls or traffic ahead while traveling straight
(driverless mode)。 > Sole collision in driverless mode, without a
trained operator in the driver’s seat.
end struck event group, Waymo vehicle moving slower while traveling
In the other collision, the Waymo vehicle, traveling straight at the
speed limit, was struck by a vehicle traveling 23 (57-35) mph
over the posted speed limit.
struck event group in right turning maneuvers
These collisions occurred while the Waymo was stationary or near
stationary waiting for crossing traffic to clear after having
gradually slowed to account for this traffic.
end struck event with braking of lead vehicle during left turn
自车在路口左转急刹停 (a deceleration to a near
stop)，后面车辆跟车距离不够来不及刹车。 > The remaining rear end
struck collision involved a deceleration to a near stop by the Waymo
Driver while making a left turn in an intersection with a following
vehicle that was traveling at a speed and following distance
that did not allow for the following driver to successfully
respond to the Waymo Driver’s braking.
Rear end striking event
motive)，在前方没有障碍物的情况下，故意插入 (cut in) 自车前方后急刹
> The single simulated event in this grouping involved a vehicle that
swerved into the lane in front of the Waymo and braked
hard immediately after cutting in despite lack of any obstruction ahead
(consistent with antagonistic motive).
Multiple Vehicle Events:
Angled collisions, those that are typically seen at intersections and
involve crossing or turning vehicles, account for approximately
one quarter of all human-driven collisions and a
similar fraction of the contribution to all human-driven fatalities.
event group with the other vehicle not yielding to Waymo
The collisions in this grouping (ten simulated, one actual) involve
the Waymo vehicle traveling straight in a designated
lane at or below the speed limit. In all scenarios, the
turning/crossing other vehicle either disregarded traffic controls or
otherwise did not properly yield right-of-way.
路权的定义： > Right-of-way is determined based on the
positions of vehicles prior to contact with respect to the
intersection geometry, roadway markings, and the status
of traffic control devices. Right-of-way is useful as a means of
categorizing some events, but it can be insufficient to
determine collision responsibilities since it does not reflect
all road rule violations (e.g. speeding), nor does it provide
information regarding collision avoidability.
为了减少碰撞风险，Waymo 车辆即使在有路权时也会让行 (yielding)。 >
In order to avoid collisions, the Waymo Driver recognizes that
yielding even when the Waymo vehicle is entitled to right-of-way
may be more appropriate to decrease the risk of collision, for
example when encountering an incautious other agent.
In both instances, when the simulated Waymo Driver became aware of
the other vehicle's intention to enter the travel lane, the simulated
Waymo Driver initiated braking in an attempt to avoid/mitigate impact. *
如果检查到其他车不让行，那么 Waymo 自车会开始刹车来避免碰撞。
目前的做法也只能是安全员接管。 > The simulated collision in Figure 9
(Event H) depicts a vehicle making a left turn across the Waymo
vehicle’s travel path. The Waymo Driver’s simulated response to the
vehicle’s action was the initiation of braking just prior to entering
这是最严重的一类碰撞事故 (the most severe collision)。 > It is the
most severe collision (simulated or actual) in the dataset and
approaches the boundary between S1 and S2 classification.
event group with Waymo vehicle crossing another vehicle’s path
The collisions in this grouping involve four
simulated collisions, where the Waymo Driver was making a right turn
from a rightmost lane that was either splitting to an additional lane,
or had been the result of two lanes merging to one.
> In each event, a passenger vehicle attempted to pass the Waymo
vehicle on the right while the Waymo Driver was slowing to make the
right turn with the right turn signal activated.
> The goal of this transparency is to contribute to broad learning
with the industry, policymakers, and the public; promote awareness and
discussions; and foster greater public confidence in automated
Of the fifteen angled events, eleven events were characterized by the
other vehicle failing to properly yield right-of-way to the Waymo
vehicle traveling straight at or below the speed limit. > MC:
Avoidance: Management of Human-Driver-Related Contributing Factors
> Humans exhibit a large variation of driving behaviors including
deviations from traffic rules and safe driving
performance that can lead to collisions.
> Nearly all events summarized above involved one or more road rule
violations or other driving performance deviations by another
(inattention)，激进驾驶 (aggressive driving) 和超速 (speeding)
带来的可能碰撞。 > In addition to Waymo's key focus on not causing
collisions, Waymo also works to mitigate possible collisions due to
human behaviors such as inattention, aggressive driving, and
Although many of these situations would not be present in a
future with a high proportion of AVs, we envision sharing roads with
human drivers for the foreseeable future. The rare contact
events described in this paper are used to develop enhanced collision
avoidance to improve traffic safety, and we will continue to
focus on enhancing avoidance of human-induced collisions. >
(other road users) 可判断 (interpretable) 和可预测 (predictable)。 >
Beyond collision avoidance, Waymo also continually investigates
improvements to the Waymo Driver’s behaviors to reduce the likelihood of
conflict with human-driven vehicles and other road users.
> This illustrates a key challenge faced by AVs operating in a
predominantly human traffic system and underscores the
importance of driving in a way that is interpretable and predictable by
other road users.
的自动驾驶能力是可以不断提升的，适用到整个车队上。 > Unlike human
drivers, who primarily improve through individual
experience, the learnings from an event experienced by a single
AV can be used to permanently improve the safety performance of
an entire fleet of AVs.
Aggregate Safety Performance
Waymo 车辆在单车表现 (single-vehicle collision typology) 和 追尾问题
(rear-end collisions) 上的良好表现，已经优于人类。 > The Waymo Driver
experienced zero actual or simulated events in the “road departure,
fixed object, rollover” single-vehicle collision typology, reflecting
the system's ability to navigate the ODD in a highly reliable
> In addition, while rear-end collisions are one of the most common
collision modes for human drivers, the Waymo Driver only recorded
a single front-to-rear striking collision (simulated)
and this event involved an agent cutting in and immediately braking
without allowing for adequate separation distance (consistent with
无论是人类驾驶员，还是自动驾驶车辆，轻微事故 (lower-severity events)
发生的频率要高于严重事故 (higher-severity) 的发生频率。 > In both
human-driven and automated vehicles, lower-severity events (S0 and S1)
occur at significantly higher frequency than higher-severity (S2 and S3)
events. As a result, fewer miles are needed to draw statistical
conclusions about S0 and S1 rates.
> When comparing driving data, the mileage needed to reveal
statistically significant differences also depends on the
magnitude of the differences in the actual rates being
For a given metric, the larger the difference in performance, the
fewer miles that are required to establish statistical
confidence in a hypothesis of non-inferiority or
如何统计和判责 > low-severity data, when evaluated in the context
of each event’s collision geometry, may be informative of high-severity
现有的道路公开测试，可以为 S0 or S1 提供统计上的支持 (sufficient
statistical signal)。 > The 6.1 million miles in self-driving with
trained operators mode underlying the data in Section 3 provide
sufficient statistical signal to detect moderate-to-large differences in
S0 and S1 event frequencies, and Waymo makes use of these event
rates for tracking longer-term improvements to the Waymo Driver.
现有的道路公开测试，无法为 S2 or S3 提供强有力的统计支持。 > 6.1
million miles does not provide statistical power to draw meaningful
conclusions about the frequencies of events of severity S2 or S3.
> MC：目前的测试里程，可以为 Lower-severity
提供支持，但是不能为偶发的 Higher-severity 提供支持，里面有统计噪声
> At this mileage scale, the statistical noise is extremely large and
zero or low event counts only provide performance
bounds, which necessitates the consideration of other metrics
to fully assess the safety of the Waymo Driver.
通过仿真和封闭场测试来评估高风险的表现. > Waymo uses other
methods to evaluate the higher-severity performance, including both
simulation-based and closed-course scenario-based collision-avoidance
从低风险事故中发掘高风险事故的信息。 > Low-severity data, when
evaluated in the context of each event’s collision geometry, may be
informative of high-severity risk.
Human driver collision rates have been widely discussed as providing
a benchmark for AVs.
contact)，所以 police-reported 不足以代表人类的真实事故发生频率。 >
By including low-speed events involving non-police-reportable contact
(e.g. a less than 2 mph vehicle-to-vehicle contact or a pedestrian
walking into the side of a stationary vehicle), the scope of
events is considerably greater than the scope of police-reported or
insurance-reported collisions commonly used to generate performance
baselines. As such, comparing the data presented in
this paper to police-reported collision numbers is not an apt
Obtaining reliable event counts that include such minor events
typically requires analysis of naturalistic driving
Limitations and Future Work
Limitations related to the statistical power of the mileages reported
have been discussed in the above section on aggregate collision
frequencies. > 即，目前的测试里程，可以为 Lower-severity
提供支持，但是不能为偶发的 Higher-severity 提供支持，里面有统计噪声
反事实仿真只是预测，但不是绝对的准确。 > Due to the nature of
human agent behavior, disengagement simulations are not definitive:
counterfactual simulations predict what could have occurred, but cannot
definitively predict exactly what would have occurred.
> As a result, had the driver not disengaged, some of the reported
simulated collisions may not have actually occurred (e.g. other agents
may have behaved differently). Conversely, other events that, in
simulation did not result in contact, may have actually resulted in
collisions (e.g. if the other agent had been distracted at the critical
Waymo therefore takes a cautious approach to
interpreting both the outcomes of individual collisions and aggregate
performance metrics, and considers them in the context of other
indicators of AV performance.
Secondary collision in
The severities ascribed to the simulated collisions are based
on the single impact depicted in the simulation. Owing to
complexities in accurately modeling post-impact vehicle dynamics (which
may or may not involve subsequent steering and braking maneuvers from
the other vehicle), the outcome of any secondary collisions that might
occur subsequent to the simulated event are not explicitly modeled.
> In Waymo’s ODD, the vast majority of primary vehicle-to-vehicle
collisions (99% for all collisions, 95% for fatal collisions)
included in police-reported crash databases involve either a
single vehicle-to-vehicle collision event or a subsequent collision
event of equal or lesser severity.
Waymo 安全驾驶员的避障表现，不代表人类驾驶员的避障表现。 > Care
should be taken in drawing conclusions based on the collision-avoidance
performance of Waymo’s trained operators during disengagements, which
for the reasons described below, is not predictive of the
collision-avoidance performance of the overall population of human
受过专业培训： Waymo vehicle operators are selected from a subset of
the driving population with good driving records and receive instruction
specific to Waymo AVs, defensive driving training, and
education regarding fatigue.
避免被打扰：When operating a vehicle, strict rules are in place
regarding handheld devices including cell phones and operators are
continually monitored for signs of drowsiness.
注意力更集中：Unlike drivers in human-driven vehicles, while the AV
is in self-driving mode, Waymo’s trained operators do not execute
navigation, path planning, or control tasks, but instead are focused on
monitoring the environment and the Waymo Driver’s response to it.
Trained vehicle operators are therefore able to focus their full
attention on being ready to disengage and execute collision avoidance,
and their performance at this task is expected to be superior to
that of a human in a traditional driving role.
We expect and invite other safety researchers to review the events
and mileages presented here and make their own findings regarding the
safety performance of Waymo’s operations demonstrated in this data.
Taken together, these 47 lower severity (S0 and S1) events (18 actual
and 29 simulated, one during driverless operation) show
significant contribution from other agents, namely
human-related deviations from traffic rules and safe driving
The frequency of challenging events that were induced by incautious
behaviors of other drivers serves as a clear reminder of the
challenges in collision avoidance so long as AVs share roadways with
(inflated expectations)。 > Statistics regarding the high percentage
of human collisions that are attributed to human error may lead to
inflated expectations of the potential immediate safety benefits of AVs.
AVs will share roads with human drivers for the foreseeable future, and
significant numbers of collisions due to human driver errors that are
simply unavoidable should be expected during this
> Due to the typology of those collisions initiated by other actors
as well as the Waymo Driver’s proficiency in avoiding certain collision
modes, the data presented shows a significant shift in
the relative distributions of collision types as compared to national
crash statistics for human drivers.
> This is the first time that information on every actual and
simulated collision or contact has been shared for millions of miles of
> The most significant long-term contributions of this paper will
likely not be the actual data shared, but the example set by publicly
sharing this type of safety performance data and the dialogs that this
Web of Science 和 Google Scholar 上根据关键词去找所需要的文献。 > Web
Web of Science
We have a checkbox in the Document Details panel that allows you to
keep that entry from being aggregated. It will still be synchronized
across your multiple devices, but it will not have the Document Details
aggregated to our research catalog.
In summary, if you’re adding a document and you don’t want the
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search in our research catalog, then go ahead and click on the
“Unpublished work” checkbox in the Document Details panel on the
It's to be an Autonomous Driving Engineer in the future. Autonomous
Driving is transforming the transport, which can lessen the traffic
problems caused by human drivers, a very meaningful career. Besides, I
can put what I have learned at school into practice.
What's you favorite weather?
I like sunny ['sʌnɪ adj.阳光充足的;快活的] weather in the daytime,
since I can spend time outdoors without suffering ['sʌfə
vi.遭受,忍受;受痛苦] from the cold & rain. Yeah, I enjoy practicing
sports and ==being out in the nature== because it makes me feel alive
What you did last week? > I finished /ˈfɪnɪʃt/ a slide
presentation in my work, and I read a book called <Poor dad, Rich
dad> in the weekend, and it inspired me on many
How often do you use you mobile phone? > On a daily basis, almost
every moment. I like using my mobile phone to contact /ˈkɒntækt/ my
friends online and keep up to date with them, and reading the latest
news from the internet. I can have a good relationship with my friends
and know what happens in the world via /ˈvaɪə/ prep. 经由，通过 a mobile