Conference Paper

The MIT – Cornell Collision and Why It Happened

Conference: The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA
Source: DBLP


Mid-way through the 2007 DARPA Urban Challenge, MIT’s autonomous Land
Rover LR3 ‘Talos’ and Team Cornell’s autonomous Chevrolet Tahoe ‘Skynet’ collided
in a low-speed accident, one of the first well-documented collisions between
two full-size autonomous vehicles. This collaborative study between MIT and Cornell
examines the root causes of the collision, which are identified in both teams’
system designs. Systems-level descriptions of both autonomous vehicles are given,
and additional detail is provided on sub-systems and algorithms implicated in the
collision. A brief summary of robot–robot interactions during the race is presented,
followed by an in-depth analysis of both robots’ behaviors leading up to and during
the Skynet–Talos collision. Data logs from the vehicles are used to show the
gulf between autonomous and human-driven vehicle behavior at low speeds and
close proximities. Contributing factors are shown to be: (1) difficulties in sensor
data association leading to phantom obstacles and an inability to detect slow moving
vehicles, (2) failure to anticipate vehicle intent, and (3) an over emphasis on
lane constraints versus vehicle proximity in motion planning. Eye contact between
human road users is a crucial communications channel for slow-moving close encounters
between vehicles. Inter-vehicle communication may play a similar role for
autonomous vehicles; however, there are availability and denial-of-service issues to
be addressed.

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    • "The centre line of the determined corridor forms the path around which the trajectory to be followed by an autonomous vehicle is planned. The major drawback of planning in a continuous way is that, since intensive computational power 3 is needed for planning for the entire range of coordinates regarding the road network, representation of roads or lanes may constrain the motion of the vehicle (Fletcher et al., 2008). "
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    ABSTRACT: Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments.
    Full-text · Article · Nov 2015 · Transportation Research Part C Emerging Technologies
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    • "2 They demonstrate robotics systems traveling significant distances at high speed in complex and realistic environments. However such systems remains prone to accidents (see Fletcher et al. 2008). While moving (especially at high speed), AGVs (and other robotic systems as well) can be potentially dangerous should a collision occur; this is a critical issue if such systems are to transport or share space with human beings. "
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    ABSTRACT: This paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is the concept of Braking Inevitable Collision States (ICS), i.e. a version of the ICS corresponding to passive motion safety. Braking ICS are defined as states such that, whatever the future braking trajectory followed by the robot, a collision occurs before it is at rest. Passive motion safety is obtained by avoiding Braking ICS at all times. It is shown that Braking ICS verify properties that allow the design of an efficient Braking ICS-Checking algorithm, i.e. an algorithm that determines whether a given state is a Braking ICS or not. To validate the Braking ICS concept and demonstrate its usefulness, the Braking ICS-Checking algorithm is integrated in a reactive navigation scheme called PassAvoid. It is formally established that PassAvoid is provably passively safe in the sense that it is guaranteed that the robot will always stay away from Braking ICS no matter what happens in the environment.
    Full-text · Article · Apr 2012 · Autonomous Robots
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    • "Within this perspective, driver intention estimation (and more generally situation assessment) has been identied as a key problem for intelligent vehicles, and one of the three main remaining challenges [1]. Indeed, trac at intersections is highly dynamic and involves complex interactions between vehicles. "
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    ABSTRACT: This work proposes a novel approach to risk assessment at road intersections. Unlike most approaches in the literature, it does not rely on trajectory prediction. Instead, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do. What a driver intends to do is estimated from the motion of the vehicle, taking into account the layout of the intersection. What a driver is expected to do is derived from the current configuration of the vehicles and the traffic rules at the intersection. The proposed approach was validated in simulation and in field experiments using passenger vehicles and Vehicle-to-Vehicle communication. Different strategies are compared to actively avoid collisions if a dangerous situation is detected. The results show that the effectiveness of the strategies varies with the situation.
    Full-text · Article · Mar 2012
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